From 25dd9f432f2e593c79d80d49d092b68d4de93739 Mon Sep 17 00:00:00 2001 From: xumengwen <421269175@qq.com> Date: Mon, 17 Jun 2024 09:52:43 +0000 Subject: [PATCH] first commit --- .gitmodules | 12 + CODE_OF_CONDUCT.md | 9 + LICENSE | 21 + README.md | 305 +++ SECURITY.md | 41 + config/default/autoformer.yaml | 45 + config/default/csdi.yaml | 45 + config/default/dlinear.yaml | 32 + config/default/gru.yaml | 34 + config/default/gru_maf.yaml | 42 + config/default/gru_nvp.yaml | 42 + config/default/itransformer.yaml | 41 + config/default/linear.yaml | 29 + config/default/mean.yaml | 28 + config/default/nhits.yaml | 43 + config/default/nlinear.yaml | 31 + config/default/patchtst.yaml | 37 + config/default/timegrad.yaml | 40 + config/default/timesnet.yaml | 36 + config/default/trans_maf.yaml | 46 + config/default/transformer.yaml | 38 + config/default/tsdiff.yaml | 44 + config/ltsf/electricity_ltsf/csdi.yaml | 45 + config/ltsf/electricity_ltsf/dlinear.yaml | 35 + 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probts/model/nn/layers/Moirai_backbone.py create mode 100644 probts/model/nn/layers/PatchTST_backbone.py create mode 100644 probts/model/nn/layers/PatchTST_layers.py create mode 100644 probts/model/nn/layers/RevIN.py create mode 100644 probts/model/nn/layers/SelfAttention_Family.py create mode 100644 probts/model/nn/layers/Transformer_EncDec.py create mode 100644 probts/model/nn/layers/__init__.py create mode 100644 probts/model/nn/layers/diffusion_layers.py create mode 100644 probts/model/nn/layers/s4.py create mode 100644 probts/model/nn/layers/s4_backbones.py create mode 100644 probts/utils/__init__.py create mode 100644 probts/utils/download_datasets.py create mode 100644 probts/utils/evaluator.py create mode 100644 probts/utils/masking.py create mode 100644 probts/utils/metrics.py create mode 100644 probts/utils/utils.py create mode 100644 pyproject.toml create mode 100644 run.py create mode 100644 run.sh create mode 100644 run_tsfm.sh create mode 160000 submodules/lag_llama create mode 160000 submodules/timesfm create mode 160000 submodules/tsfm create mode 160000 submodules/uni2ts diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..ad01e60 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,12 @@ +[submodule "submodules/uni2ts"] + path = submodules/uni2ts + url = https://github.com/SalesforceAIResearch/uni2ts.git +[submodule "submodules/lag_llama"] + path = submodules/lag_llama + url = https://github.com/time-series-foundation-models/lag-llama.git +[submodule "submodules/timesfm"] + path = submodules/timesfm + url = https://github.com/google-research/timesfm.git +[submodule "submodules/tsfm"] + path = submodules/tsfm + url = https://github.com/ibm-granite/granite-tsfm.git diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..f9ba8cf --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,9 @@ +# Microsoft Open Source Code of Conduct + +This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). + +Resources: + +- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) +- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) +- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..9e841e7 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ + MIT License + + Copyright (c) Microsoft Corporation. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE diff --git a/README.md b/README.md new file mode 100644 index 0000000..80b173b --- /dev/null +++ b/README.md @@ -0,0 +1,305 @@ +
+ +# ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons + +---------- +[ [Paper](https://arxiv.org/abs/2310.07446) | [Benchmarking](./docs/benchmark/README.md) | [Documentation](./docs/documentation/README.md) ] + +A wide range of industrial applications desire precise point and distributional forecasting for diverse prediction horizons. ProbTS serves as a benchmarking tool to aid in understanding how advanced time-series models fulfill these essential forecasting needs. It also sheds light on their advantages and disadvantages in addressing different challenges and unveil the possibilities for future research. + +To achieve these objectives, ProbTS provides a unified pipeline that implements [cutting-edge models](#-available-models) from different research threads, including: +- Long-term point forecasting approaches, such as [PatchTST](https://arxiv.org/abs/2211.14730), [iTransformer](https://arxiv.org/abs/2310.06625), etc. +- Short-term probabilistic forecasting methods, such as [TimeGrad](https://arxiv.org/abs/2101.12072), [CSDI](https://arxiv.org/abs/2107.03502), etc. +- Recent time-series foundation models for universal forecasting, such as [TimesFM](https://arxiv.org/abs/2310.10688), [MOIRAI](https://arxiv.org/abs/2402.02592), etc. + +Specifically, ProbTS emphasizes the differences in their primary methodological designs, including: +- Supporting point or distributional forecasts +- Using autoregressive or non-autoregressive decoding schemes for multi-step outputs + +
+ +## Available Models 🧩 + +ProbTS includes both classical time-series models, specializing in long-term point forecasting or short-term distributional forecasting, and recent time-series foundation models that offer zero-shot and arbitrary-horizon forecasting capabilities for new time series. + +### Classical Time-series Models + +| **Model** | **Original Eval. Horizon** | **Estimation** | **Decoding Scheme** | **Class Path** | +| --- | --- | --- | --- | --- | +| Linear | - | Point | Auto / Non-auto | `probts.model.forecaster.point_forecaster.LinearForecaster` | +| [GRU](https://arxiv.org/abs/1412.3555) | - | Point | Auto / Non-auto | `probts.model.forecaster.point_forecaster.GRUForecaster` | +| [Transformer](https://arxiv.org/abs/1706.03762) | - | Point | Auto / Non-auto | `probts.model.forecaster.point_forecaster.TransformerForecaster` | +| [Autoformer](https://arxiv.org/abs/2106.13008) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.Autoformer` | +| [N-HiTS](https://arxiv.org/abs/2201.12886) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.NHiTS` | +| [NLinear](https://arxiv.org/abs/2205.13504) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.NLinear` | +| [DLinear](https://arxiv.org/abs/2205.13504) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.DLinear` | +| [TimesNet](https://arxiv.org/abs/2210.02186) | Short- / Long-term | Point | Non-auto | `probts.model.forecaster.point_forecaster.TimesNet` | +| [PatchTST](https://arxiv.org/abs/2211.14730) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.PatchTST` | +| [iTransformer](https://arxiv.org/abs/2310.06625) | Long-trem | Point | Non-auto | `probts.model.forecaster.point_forecaster.iTransformer` | +| [GRU NVP](https://arxiv.org/abs/2002.06103) | Short-term | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.GRU_NVP` | +| [GRU MAF](https://arxiv.org/abs/2002.06103) | Short-term | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.GRU_MAF` | +| [Trans MAF](https://arxiv.org/abs/2002.06103) | Short-term | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.Trans_MAF` | +| [TimeGrad](https://arxiv.org/abs/2101.12072) | Short-term | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.TimeGrad` | +| [CSDI](https://arxiv.org/abs/2107.03502) | Short-term | Probabilistic | Non-auto | `probts.model.forecaster.prob_forecaster.CSDI` | +| [TSDiff](https://arxiv.org/abs/2307.11494) | Short-term | Probabilistic | Non-auto | `probts.model.forecaster.prob_forecaster.TSDiffCond` | + +### Fundation Models + +| **Model** | **Any Horizon** | **Estimation** | **Decoding Scheme** | **Class Path** | +| --- | --- | --- | --- | --- | +| [Lag-Llama](https://arxiv.org/abs/2310.08278) | ✔ | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.LagLlama` | +| [ForecastPFN](https://arxiv.org/abs/2311.01933) | ✔ | Point | Non-auto | `probts.model.forecaster.point_forecaster.ForecastPFN` | +| [TimesFM](https://arxiv.org/abs/2310.10688) | ✔ | Point | Auto | `probts.model.forecaster.point_forecaster.TimesFM` | +| [TTM](https://arxiv.org/abs/2401.03955) | ✘ | Point | Non-auto | `probts.model.forecaster.point_forecaster.TinyTimeMixer` | +| [Timer](https://arxiv.org/abs/2402.02368) | ✔ | Point | Auto | `probts.model.forecaster.point_forecaster.Timer` | +| [MOIRAI](https://arxiv.org/abs/2402.02592) | ✔ | Probabilistic | Non-auto | `probts.model.forecaster.prob_forecaster.Moirai` | +| [UniTS](https://arxiv.org/abs/2403.00131) | ✔ | Point | Non-auto | `probts.model.forecaster.point_forecaster.UniTS` | +| [Chronos](https://arxiv.org/abs/2403.07815) | ✔ | Probabilistic | Auto | `probts.model.forecaster.prob_forecaster.Chronos` | + +Stay tuned for more models to be added in the future. + + +## Setup :wrench: + +### Environment + +ProbTS is developed with Python 3.10 and relies on [PyTorch Lightning](https://github.com/Lightning-AI/lightning). To set up the environment: + +```bash +# Create a new conda environment +conda create -n probts python=3.10 +conda activate probts + +# Install required packages +pip install . +pip uninstall -y probts # recommended to uninstall the root package (optional) +``` + +[Optional] For time-series foundation models, you need to install basic packages and additional dependencies: + +```bash +# Create a new conda environment +conda create -n probts_fm python=3.10 +conda activate probts_fm + +# Install required packages +pip install . + +# Git submodule +git submodule update --init --recursive + +# Install additional packages for foundation models +pip install ".[tsfm]" +pip uninstall -y probts # recommended to uninstall the root package (optional) + +# For MOIRAI, we fix the version of the package for better performance +cd submodules/uni2ts +git reset --hard fce6a6f57bc3bc1a57c7feb3abc6c7eb2f264301 +``` + +
+ +Optional for TSFMs reproducibility + +```bash +# For TimesFM, fix the version for reproducibility (optional) +cd submodules/timesfm +git reset --hard 5c7b905 + +# For Lag-Llama, fix the version for reproducibility (optional) +cd submodules/lag_llama +git reset --hard 4ad82d9 + +# For TinyTimeMixer, fix the version for reproducibility (optional) +cd submodules/tsfm +git reset --hard bb125c14a05e4231636d6b64f8951d5fe96da1dc +``` + +
+ +### Datasets + +- **Short-Term Forecasting**: We use datasets from [GluonTS](https://github.com/awslabs/gluonts). + Configure the datasets using `--data.data_manager.init_args.dataset {DATASET_NAME}`. You can choose from multivariate or univariate datasets as per your requirement. + ```bash + # Multivariate Datasets + ['exchange_rate_nips', 'electricity_nips', 'traffic_nips', 'solar_nips', 'wiki2000_nips'] + + # Univariate Datasets + ['tourism_monthly', 'tourism_quarterly', 'tourism_yearly', 'm4_hourly', 'm4_daily', 'm4_weekly', 'm4_monthly', 'm4_quarterly', 'm4_yearly', 'm5'] + ``` + +- **Long-Term Forecasting**: To set up the long-term forecasting datasets, please follow these steps: + 1. Download long-term forecasting datasets from [HERE](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy) and place them in `./dataset`. + 2. [Opt.] Download CAISO and NordPool datasets from [DEPTS](https://github.com/weifantt/DEPTS/tree/main) and place them in ```./dataset```. + + Configure the datasets using `--data.data_manager.init_args.dataset {DATASET_NAME}` with the following list of available datasets: + ```bash + # Long-term Forecasting + ['etth1', 'etth2','ettm1','ettm2','traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'illness_ltsf', 'weather_ltsf', 'caiso', 'nordpool'] + ``` + Note: When utilizing long-term forecasting datasets, you must explicitly specify the `context_length` and `prediction_length` parameters. For example, to set a context length of 96 and a prediction length of 192, use the following command-line arguments: + ```bash + --data.data_manager.init_args.context_length 96 \ + --data.data_manager.init_args.prediction_length 192 \ + ``` + + +### Checkpoints for Foundation Models + +For full reproducibility, we provide the checkpoints for some foundation models as of the paper completion date. Download the checkpoints from [here](https://drive.google.com/drive/folders/1FaCk9Lj9KZGEO09gehNqC4fbTj4wnN8j?usp=sharing) and place them in the ./checkpoints folder. + +
+You can also download the newest checkpoints from the following repositories: + +- For `Timer`, download the checkpoints from its [official repository](https://github.com/thuml/Large-Time-Series-Model?tab=readme-ov-file#code-for-fine-tuning) ([Google Drive](https://drive.google.com/drive/folders/15oaiAl4OO5gFqZMJD2lOtX2fxHbpgcU8) or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/235e6bfcf5fa440bb119/)) under the folder `/path/to/checkpoints/timer/`. +- For `ForecastPFN`, download the checkpoints from its [official repository](https://github.com/abacusai/ForecastPFN#installation-) ([Google Drive](https://drive.google.com/file/d/1acp5thS7I4g_6Gw40wNFGnU1Sx14z0cU/view)) under the folder `/path/to/checkpoints/forecastpfn/`. +- For `UniTS`, download the checkpoints `units_x128_pretrain_checkpoint.pth` from its [official repository](https://github.com/mims-harvard/UniTS/releases/tag/ckpt) under the folder `/path/to/checkpoints/units/`. +- For `Lag-Llama`, download the checkpoints `lag-llama.ckpt` from its [huggingface repository](https://huggingface.co/time-series-foundation-models/Lag-Llama/tree/main) under the folder `/path/to/checkpoints/lag_llama/`. +- For other models, they can be automatically downloaded from huggingface during the first run. + +| **Model** | **HuggingFace** | +| --- | --- | +| `MOIRAI` | [Link](https://huggingface.co/Salesforce/moirai-1.0-R-small) | +| `Chronos` | [Link](https://huggingface.co/amazon/chronos-t5-large) | +| `TinyTimeMixer` | [Link](https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1) | +| `TimesFM` | [Link](https://huggingface.co/google/timesfm-1.0-200m) | + +
+ +## Quick Start :rocket: + +Specify `--config` with a specific configuration file to reproduce results of point or probabilistic models on commonly used long- and short-term forecasting datasets. Configuration files are included in the [config](./config/) folder. + +To run non-universal models: +```bash +bash run.sh +``` + +To run foundation models: +```bash +bash run_tsfm.sh +``` + +### Short-term Forecasting + +For short-term forecasting scenarios, datasets and corresponding `context_length` and `prediction_length` are automatically obtained from [GluonTS](https://github.com/awslabs/gluonts). Use the following command: + +```bash +python run.py --config config/path/to/model.yaml \ + --data.data_manager.init_args.path /path/to/datasets/ \ + --trainer.default_root_dir /path/to/log_dir/ \ + --data.data_manager.init_args.dataset {DATASET_NAME} +``` +See full `DATASET_NAME` list: +```python +from gluonts.dataset.repository import dataset_names +print(dataset_names) +``` + +### Long-term Forecasting + +For long-term forecasting scenarios, `context_length` and `prediction_length` must be explicitly assigned: + +```bash +python run.py --config config/path/to/model.yaml \ + --data.data_manager.init_args.path /path/to/datasets/ \ + --trainer.default_root_dir /path/to/log_dir/ \ + --data.data_manager.init_args.dataset {DATASET_NAME} \ + --data.data_manager.init_args.context_length {CTX_LEN} \ + --data.data_manager.init_args.prediction_length {PRED_LEN} +``` + +`DATASET_NAME` options: +```bash +['etth1', 'etth2','ettm1','ettm2','traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'illness_ltsf', 'weather_ltsf', 'caiso', 'nordpool'] +``` + +## Benchmarking :balance_scale: + +By utilizing ProbTS, we conduct a systematic comparison between studies that focus on point forecasting and those aimed at distributional estimation, employing various forecasting horizons and evaluation metrics. For more details + +- [Short-term & Long-term Forecasting Benchmarking](./docs/benchmark/README.md) +- [Evaluating Time Series Foundation Models](./docs/benchmark/FOUNDATION_MODEL.md) + + +## Documentation :open_book: + +For detailed information on configuration parameters and model customization, please refer to the [documentation](./docs/documentation/README.md). + + +### Key Configuration Parameters + +- Adjust model and data parameters in `run.sh`. Key parameters include: + +| Config Name | Type | Description | +| --- | --- | --- | +| `trainer.max_epochs` | integer | Maximum number of training epochs. | +| `model.forecaster.class_path` | string | Forecaster module path (e.g., `probts.model.forecaster.point_forecaster.PatchTST`). | +| `model.forecaster.init_args.{ARG}` | - | Model-specific hyperparameters. | +| `model.num_samples` | integer | Number of samples per distribution during evaluation. | +| `model.learning_rate` | float | Learning rate. | +| `data.data_manager.init_args.dataset` | string | Dataset for training and evaluation. | +| `data.data_manager.init_args.path` | string | Path to the dataset folder. | +| `data.data_manager.init_args.scaler` | string | Scaler type: `identity`, `standard` (z-score normalization), or `temporal` (scale based on average temporal absolute value). | +| `data.data_manager.init_args.context_length` | integer | Length of observation window (required for long-term forecasting). | +| `data.data_manager.init_args.prediction_length` | integer | Forecasting horizon length (required for long-term forecasting). | +| `data.data_manager.init_args.var_specific_norm` | boolean | If conduct per-variate normalization or not. | +| `data.batch_size` | integer | Batch size. | + + +- To print the full pipeline configuration to a file: + + ```bash + python run.py --print_config > config/pipeline_config.yaml + ``` + +## Acknowledgement 🌟 + +Special thanks to the following repositories for their open-sourced code bases and datasets. + +### Tools/Packages + +- [GluonTS](https://github.com/awslabs/gluonts) +- [PyTorch-TS](https://github.com/zalandoresearch/pytorch-ts) +- [TSLib](https://github.com/libts/tslib) +- [NeuralForecast](https://github.com/Nixtla/neuralforecast) + +### Official Implementations + +**Classical Time-series Models** + +- [Autoformer](https://github.com/thuml/Autoformer) +- [N-HiTS](https://github.com/cchallu/n-hits) +- [NLinear, DLinear](https://github.com/cure-lab/LTSF-Linear) +- [TimesNet](https://github.com/thuml/Time-Series-Library) +- [RevIN](https://github.com/ts-kim/RevIN) +- [PatchTST](https://github.com/yuqinie98/PatchTST) +- [iTransformer](https://github.com/thuml/iTransformer) +- [GRU NVP, GRU MAF, Trans MAF, TimeGrad](https://github.com/zalandoresearch/pytorch-ts/tree/master) +- [CSDI](https://github.com/ermongroup/CSDI) +- [TSDiff](https://github.com/amazon-science/unconditional-time-series-diffusion) + + +**Time-series Foundation Models** + +- [MOIRAI](https://github.com/SalesforceAIResearch/uni2ts) +- [Chronos](https://github.com/amazon-science/chronos-forecasting) +- [Lag-Llama](https://github.com/time-series-foundation-models/lag-llama) +- [TimesFM](https://github.com/google-research/timesfm) +- [Timer](https://github.com/thuml/Large-Time-Series-Model) +- [UniTS](https://github.com/mims-harvard/UniTS) +- [ForecastPFN](https://github.com/abacusai/ForecastPFN) +- [TTM](https://github.com/ibm-granite/granite-tsfm) + +## Citing ProbTS 🌟 + +If you have used ProbTS for research or production, please cite it as follows. +```tex +@article{zhang2023probts, + title={{ProbTS}: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons}, + author={Zhang, Jiawen and Wen, Xumeng and Zhang, Zhenwei and Zheng, Shun and Li, Jia and Bian, Jiang}, + journal={arXiv preprint arXiv:2310.07446}, + year={2023} +} +``` \ No newline at end of file diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..b3c89ef --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,41 @@ + + +## Security + +Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet) and [Xamarin](https://github.com/xamarin). + +If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/security.md/definition), please report it to us as described below. + +## Reporting Security Issues + +**Please do not report security vulnerabilities through public GitHub issues.** + +Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/security.md/msrc/create-report). + +If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/security.md/msrc/pgp). + +You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). + +Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: + + * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) + * Full paths of source file(s) related to the manifestation of the issue + * The location of the affected source code (tag/branch/commit or direct URL) + * Any special configuration required to reproduce the issue + * Step-by-step instructions to reproduce the issue + * Proof-of-concept or exploit code (if possible) + * Impact of the issue, including how an attacker might exploit the issue + +This information will help us triage your report more quickly. + +If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/security.md/msrc/bounty) page for more details about our active programs. + +## Preferred Languages + +We prefer all communications to be in English. + +## Policy + +Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/security.md/cvd). + + diff --git a/config/default/autoformer.yaml b/config/default/autoformer.yaml new file mode 100644 index 0000000..25428ea --- /dev/null +++ b/config/default/autoformer.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 + # num_sanity_val_steps: 0 + # gradient_clip_algorithm: 'norm' +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.Autoformer + init_args: + moving_avg: 25 + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + d_layers: 1 + output_attention: false + d_ff: 512 + f_hidden_size: 512 + embed: 'timeF' + use_lags: false + use_feat_idx_emb: false + use_time_feat: true + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-3 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # none, standard, scaling + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/default/csdi.yaml b/config/default/csdi.yaml new file mode 100644 index 0000000..9c27587 --- /dev/null +++ b/config/default/csdi.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 1 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 128 + emb_feature_dim: 16 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 128 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 4 + test_batch_size: 4 + num_workers: 8 diff --git a/config/default/dlinear.yaml b/config/default/dlinear.yaml new file mode 100644 index 0000000..3bd35fe --- /dev/null +++ b/config/default/dlinear.yaml @@ -0,0 +1,32 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 3 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + learning_rate: 0.01 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/default/gru.yaml b/config/default/gru.yaml new file mode 100644 index 0000000..d1cf880 --- /dev/null +++ b/config/default/gru.yaml @@ -0,0 +1,34 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.GRUForecaster + init_args: + f_hidden_size: 40 + num_layers: 2 + dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/default/gru_maf.yaml b/config/default/gru_maf.yaml new file mode 100644 index 0000000..53fee8f --- /dev/null +++ b/config/default/gru_maf.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 1 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_MAF + init_args: + enc_num_layers: 2 + enc_hidden_size: 40 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: false + conditional_length: 200 + dequantize: true + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + scaler: identity # identity, standard, temporal + split_val: true + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/default/gru_nvp.yaml b/config/default/gru_nvp.yaml new file mode 100644 index 0000000..75860b5 --- /dev/null +++ b/config/default/gru_nvp.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 7 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 40 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: true + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/default/itransformer.yaml b/config/default/itransformer.yaml new file mode 100644 index 0000000..0e2bb33 --- /dev/null +++ b/config/default/itransformer.yaml @@ -0,0 +1,41 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.iTransformer + init_args: + factor: 1 + n_heads: 8 + activation: 'gelu' + e_layers: 2 + output_attention: false + f_hidden_size: 256 + d_ff: 256 + label_len: 48 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 1 + learning_rate: 1e-4 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # none, standard, scaling + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/default/linear.yaml b/config/default/linear.yaml new file mode 100644 index 0000000..af0c832 --- /dev/null +++ b/config/default/linear.yaml @@ -0,0 +1,29 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 30 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.LinearForecaster + init_args: + individual: false + use_lags: true + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/default/mean.yaml b/config/default/mean.yaml new file mode 100644 index 0000000..e114886 --- /dev/null +++ b/config/default/mean.yaml @@ -0,0 +1,28 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.MeanForecaster + init_args: + mode: global + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/default/nhits.yaml b/config/default/nhits.yaml new file mode 100644 index 0000000..e6a16ba --- /dev/null +++ b/config/default/nhits.yaml @@ -0,0 +1,43 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.NHiTS + init_args: + n_blocks: [1,1,1] + hidden_size: 512 + pooling_mode: 'max' + interpolation_mode: 'linear' + activation: 'ReLU' + initialization: 'lecun_normal' + batch_normalization: false + shared_weights: false + naive_level: + dropout: 0 + n_layers: 2 + use_lags: false + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/default/nlinear.yaml b/config/default/nlinear.yaml new file mode 100644 index 0000000..b9edac8 --- /dev/null +++ b/config/default/nlinear.yaml @@ -0,0 +1,31 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.NLinear + init_args: + individual: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.01 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/default/patchtst.yaml b/config/default/patchtst.yaml new file mode 100644 index 0000000..f773b9d --- /dev/null +++ b/config/default/patchtst.yaml @@ -0,0 +1,37 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 3 + patch_len: 6 + dropout: 0.1 + f_hidden_size: 32 + n_layers: 3 + n_heads: 8 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_rate_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/default/timegrad.yaml b/config/default/timegrad.yaml new file mode 100644 index 0000000..65c1085 --- /dev/null +++ b/config/default/timegrad.yaml @@ -0,0 +1,40 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 100 + enc_hidden_size: 128 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + scaler: identity # identity, standard, temporal + split_val: true + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/default/timesnet.yaml b/config/default/timesnet.yaml new file mode 100644 index 0000000..745a424 --- /dev/null +++ b/config/default/timesnet.yaml @@ -0,0 +1,36 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TimesNet + init_args: + n_layers: 2 + num_kernels: 6 + top_k: 5 + d_ff: 32 + dropout: 0.1 + f_hidden_size: 40 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/default/trans_maf.yaml b/config/default/trans_maf.yaml new file mode 100644 index 0000000..1641632 --- /dev/null +++ b/config/default/trans_maf.yaml @@ -0,0 +1,46 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.Trans_MAF + init_args: + enc_hidden_size: 32 + enc_num_heads: 8 + enc_num_encoder_layers: 2 + enc_num_decoder_layers: 2 + enc_dim_feedforward_scale: 4 + enc_dropout: 0.1 + enc_activation: gelu + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: false + conditional_length: 200 + dequantize: true + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + scaler: identity # identity, standard, temporal + split_val: true + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/default/transformer.yaml b/config/default/transformer.yaml new file mode 100644 index 0000000..00d1fef --- /dev/null +++ b/config/default/transformer.yaml @@ -0,0 +1,38 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TransformerForecaster + init_args: + f_hidden_size: 16 + num_heads: 4 + num_encoder_layers: 3 + num_decoder_layers: 3 + dim_feedforward_scale: 4 + dropout: 0.1 + activation: gelu + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/default/tsdiff.yaml b/config/default/tsdiff.yaml new file mode 100644 index 0000000..8e8c77d --- /dev/null +++ b/config/default/tsdiff.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + check_val_every_n_epoch: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 + gradient_clip_val: 0.5 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TSDiffCond + init_args: + timesteps: 100 + hidden_dim: 64 + step_emb: 128 + num_residual_blocks: 3 + dropout: 0.0 + mode: diag # diag, nplr + measure: diag # 'diag', 'diag-lin', 'diag-inv', or 'diag-legs' for diag + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + use_scaling: false + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: temporal # identity, standard, temporal + context_length: 336 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 diff --git a/config/ltsf/electricity_ltsf/csdi.yaml b/config/ltsf/electricity_ltsf/csdi.yaml new file mode 100644 index 0000000..faa5660 --- /dev/null +++ b/config/ltsf/electricity_ltsf/csdi.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 1 + check_val_every_n_epoch: 3 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 64 + emb_feature_dim: 8 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 64 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 16 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_ltsf + scaler: standard # identity, standard, temporal + split_val: true + batch_size: 4 + test_batch_size: 8 + num_workers: 8 diff --git a/config/ltsf/electricity_ltsf/dlinear.yaml b/config/ltsf/electricity_ltsf/dlinear.yaml new file mode 100644 index 0000000..955ea0c --- /dev/null +++ b/config/ltsf/electricity_ltsf/dlinear.yaml @@ -0,0 +1,35 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 200 + log_every_n_steps: 1 + accumulate_grad_batches: 2 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinearEncoder + init_args: + individual: true + kernel_size: 25 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/electricity_ltsf/gru_nvp.yaml b/config/ltsf/electricity_ltsf/gru_nvp.yaml new file mode 100644 index 0000000..cd53158 --- /dev/null +++ b/config/ltsf/electricity_ltsf/gru_nvp.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 128 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 2 + hidden_size: 64 + n_hidden: 2 + batch_norm: false + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_ltsf + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/electricity_ltsf/patchtst.yaml b/config/ltsf/electricity_ltsf/patchtst.yaml new file mode 100644 index 0000000..80b0a54 --- /dev/null +++ b/config/ltsf/electricity_ltsf/patchtst.yaml @@ -0,0 +1,40 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 8 + patch_len: 16 + dropout: 0.2 + f_hidden_size: 128 + n_layers: 3 + n_heads: 16 + fc_dropout: 0.2 + head_dropout: 0 + individual: false + num_samples: 100 + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 8 + test_batch_size: 8 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/electricity_ltsf/timegrad.yaml b/config/ltsf/electricity_ltsf/timegrad.yaml new file mode 100644 index 0000000..e3d1610 --- /dev/null +++ b/config/ltsf/electricity_ltsf/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 200 + enc_hidden_size: 128 + enc_num_layers: 3 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_ltsf + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/electricity_ltsf/timesnet.yaml b/config/ltsf/electricity_ltsf/timesnet.yaml new file mode 100644 index 0000000..c908a9e --- /dev/null +++ b/config/ltsf/electricity_ltsf/timesnet.yaml @@ -0,0 +1,40 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 200 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 2 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TimesNet + init_args: + n_layers: 2 + num_kernels: 6 + top_k: 5 + d_ff: 512 + dropout: 0.1 + f_hidden_size: 256 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + num_samples: 100 + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/etth1/csdi.yaml b/config/ltsf/etth1/csdi.yaml new file mode 100644 index 0000000..b81d015 --- /dev/null +++ b/config/ltsf/etth1/csdi.yaml @@ -0,0 +1,47 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 1 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 128 + emb_feature_dim: 16 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 128 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 8 + test_batch_size: 8 + num_workers: 8 diff --git a/config/ltsf/etth1/dlinear.yaml b/config/ltsf/etth1/dlinear.yaml new file mode 100644 index 0000000..61c22ae --- /dev/null +++ b/config/ltsf/etth1/dlinear.yaml @@ -0,0 +1,35 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + accumulate_grad_batches: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: true + kernel_size: 25 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.005 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 diff --git a/config/ltsf/etth1/gru_nvp.yaml b/config/ltsf/etth1/gru_nvp.yaml new file mode 100644 index 0000000..e133f74 --- /dev/null +++ b/config/ltsf/etth1/gru_nvp.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 64 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 64 + n_hidden: 3 + batch_norm: false + conditional_length: 100 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/ltsf/etth1/patchtst.yaml b/config/ltsf/etth1/patchtst.yaml new file mode 100644 index 0000000..78eb0a9 --- /dev/null +++ b/config/ltsf/etth1/patchtst.yaml @@ -0,0 +1,39 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 8 + patch_len: 16 + dropout: 0.3 + f_hidden_size: 16 + n_layers: 3 + n_heads: 4 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/etth1/timegrad.yaml b/config/ltsf/etth1/timegrad.yaml new file mode 100644 index 0000000..0fad3cb --- /dev/null +++ b/config/ltsf/etth1/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 200 + enc_hidden_size: 128 + enc_num_layers: 3 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth1 + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/etth2/csdi.yaml b/config/ltsf/etth2/csdi.yaml new file mode 100644 index 0000000..122f487 --- /dev/null +++ b/config/ltsf/etth2/csdi.yaml @@ -0,0 +1,47 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 1 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 128 + emb_feature_dim: 16 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 128 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 8 + test_batch_size: 8 + num_workers: 8 diff --git a/config/ltsf/etth2/dlinear.yaml b/config/ltsf/etth2/dlinear.yaml new file mode 100644 index 0000000..f72a950 --- /dev/null +++ b/config/ltsf/etth2/dlinear.yaml @@ -0,0 +1,35 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + accumulate_grad_batches: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 25 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.05 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 diff --git a/config/ltsf/etth2/gru_nvp.yaml b/config/ltsf/etth2/gru_nvp.yaml new file mode 100644 index 0000000..e2db668 --- /dev/null +++ b/config/ltsf/etth2/gru_nvp.yaml @@ -0,0 +1,46 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 64 + enc_num_layers: 4 + enc_dropout: 0.1 + n_blocks: 2 + hidden_size: 128 + n_hidden: 3 + batch_norm: true + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth2 + path: /home/covpreduser/Blob/v-jiawezhang/data/all_datasets/ + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/etth2/patchtst.yaml b/config/ltsf/etth2/patchtst.yaml new file mode 100644 index 0000000..f8bae39 --- /dev/null +++ b/config/ltsf/etth2/patchtst.yaml @@ -0,0 +1,40 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 8 + patch_len: 16 + dropout: 0.3 + f_hidden_size: 16 + d_ff: 128 + n_layers: 3 + n_heads: 4 + fc_dropout: 0.2 + head_dropout: 0 + individual: false + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/etth2/timegrad.yaml b/config/ltsf/etth2/timegrad.yaml new file mode 100644 index 0000000..dad8862 --- /dev/null +++ b/config/ltsf/etth2/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 100 + enc_hidden_size: 64 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: etth2 + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/ettm1/csdi.yaml b/config/ltsf/ettm1/csdi.yaml new file mode 100644 index 0000000..ccf7b4c --- /dev/null +++ b/config/ltsf/ettm1/csdi.yaml @@ -0,0 +1,47 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 1 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 128 + emb_feature_dim: 16 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 128 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 8 + test_batch_size: 8 + num_workers: 8 diff --git a/config/ltsf/ettm1/dlinear.yaml b/config/ltsf/ettm1/dlinear.yaml new file mode 100644 index 0000000..2511626 --- /dev/null +++ b/config/ltsf/ettm1/dlinear.yaml @@ -0,0 +1,35 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + accumulate_grad_batches: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: true + kernel_size: 25 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 diff --git a/config/ltsf/ettm1/gru_nvp.yaml b/config/ltsf/ettm1/gru_nvp.yaml new file mode 100644 index 0000000..351e97d --- /dev/null +++ b/config/ltsf/ettm1/gru_nvp.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 64 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 64 + n_hidden: 3 + batch_norm: false + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm1 + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/ettm1/patchtst.yaml b/config/ltsf/ettm1/patchtst.yaml new file mode 100644 index 0000000..72c3022 --- /dev/null +++ b/config/ltsf/ettm1/patchtst.yaml @@ -0,0 +1,39 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 8 + patch_len: 16 + dropout: 0.2 + f_hidden_size: 128 + n_layers: 3 + n_heads: 16 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm1 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/ettm1/timegrad.yaml b/config/ltsf/ettm1/timegrad.yaml new file mode 100644 index 0000000..093c27a --- /dev/null +++ b/config/ltsf/ettm1/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 200 + enc_hidden_size: 128 + enc_num_layers: 3 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm1 + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/ettm2/csdi.yaml b/config/ltsf/ettm2/csdi.yaml new file mode 100644 index 0000000..87a147e --- /dev/null +++ b/config/ltsf/ettm2/csdi.yaml @@ -0,0 +1,47 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 1 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 128 + emb_feature_dim: 16 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 128 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 8 + test_batch_size: 8 + num_workers: 8 diff --git a/config/ltsf/ettm2/dlinear.yaml b/config/ltsf/ettm2/dlinear.yaml new file mode 100644 index 0000000..3802b19 --- /dev/null +++ b/config/ltsf/ettm2/dlinear.yaml @@ -0,0 +1,35 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + accumulate_grad_batches: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 25 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 diff --git a/config/ltsf/ettm2/gru_nvp.yaml b/config/ltsf/ettm2/gru_nvp.yaml new file mode 100644 index 0000000..47ab9b4 --- /dev/null +++ b/config/ltsf/ettm2/gru_nvp.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 64 + enc_num_layers: 4 + enc_dropout: 0.1 + n_blocks: 2 + hidden_size: 128 + n_hidden: 3 + batch_norm: false + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm2 + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/ettm2/patchtst.yaml b/config/ltsf/ettm2/patchtst.yaml new file mode 100644 index 0000000..0ca1579 --- /dev/null +++ b/config/ltsf/ettm2/patchtst.yaml @@ -0,0 +1,39 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 8 + patch_len: 16 + dropout: 0.2 + f_hidden_size: 128 + n_layers: 3 + n_heads: 16 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm2 + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/ettm2/timegrad.yaml b/config/ltsf/ettm2/timegrad.yaml new file mode 100644 index 0000000..cafdd05 --- /dev/null +++ b/config/ltsf/ettm2/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 200 + enc_hidden_size: 64 + enc_num_layers: 2 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: ettm2 + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/exchange_ltsf/csdi.yaml b/config/ltsf/exchange_ltsf/csdi.yaml new file mode 100644 index 0000000..f15e122 --- /dev/null +++ b/config/ltsf/exchange_ltsf/csdi.yaml @@ -0,0 +1,47 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 1 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 128 + emb_feature_dim: 16 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 128 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 8 + test_batch_size: 8 + num_workers: 8 diff --git a/config/ltsf/exchange_ltsf/dlinear.yaml b/config/ltsf/exchange_ltsf/dlinear.yaml new file mode 100644 index 0000000..374bd52 --- /dev/null +++ b/config/ltsf/exchange_ltsf/dlinear.yaml @@ -0,0 +1,35 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + accumulate_grad_batches: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: true + kernel_size: 25 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.0005 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 diff --git a/config/ltsf/exchange_ltsf/gru_nvp.yaml b/config/ltsf/exchange_ltsf/gru_nvp.yaml new file mode 100644 index 0000000..595b9e5 --- /dev/null +++ b/config/ltsf/exchange_ltsf/gru_nvp.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 128 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 2 + hidden_size: 128 + n_hidden: 3 + batch_norm: false + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_ltsf + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/exchange_ltsf/patchtst.yaml b/config/ltsf/exchange_ltsf/patchtst.yaml new file mode 100644 index 0000000..682503a --- /dev/null +++ b/config/ltsf/exchange_ltsf/patchtst.yaml @@ -0,0 +1,39 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 8 + patch_len: 16 + dropout: 0.2 + f_hidden_size: 16 + n_layers: 3 + n_heads: 4 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/exchange_ltsf/timegrad.yaml b/config/ltsf/exchange_ltsf/timegrad.yaml new file mode 100644 index 0000000..ce7b6a5 --- /dev/null +++ b/config/ltsf/exchange_ltsf/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 200 + enc_hidden_size: 64 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_ltsf + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/illness_ltsf/csdi.yaml b/config/ltsf/illness_ltsf/csdi.yaml new file mode 100644 index 0000000..97a72eb --- /dev/null +++ b/config/ltsf/illness_ltsf/csdi.yaml @@ -0,0 +1,47 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 1 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 128 + emb_feature_dim: 16 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 128 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: illness_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 8 + test_batch_size: 8 + num_workers: 8 diff --git a/config/ltsf/illness_ltsf/dlinear.yaml b/config/ltsf/illness_ltsf/dlinear.yaml new file mode 100644 index 0000000..a1d52f1 --- /dev/null +++ b/config/ltsf/illness_ltsf/dlinear.yaml @@ -0,0 +1,35 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + accumulate_grad_batches: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 25 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.01 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: illness_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 36 + prediction_length: 36 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 diff --git a/config/ltsf/illness_ltsf/gru_nvp.yaml b/config/ltsf/illness_ltsf/gru_nvp.yaml new file mode 100644 index 0000000..5926412 --- /dev/null +++ b/config/ltsf/illness_ltsf/gru_nvp.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 64 + enc_num_layers: 4 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 128 + n_hidden: 2 + batch_norm: false + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: illness_ltsf + split_val: true + scaler: identity # identity, standard, temporal + context_length: 36 + prediction_length: 36 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/illness_ltsf/patchtst.yaml b/config/ltsf/illness_ltsf/patchtst.yaml new file mode 100644 index 0000000..729ec2f --- /dev/null +++ b/config/ltsf/illness_ltsf/patchtst.yaml @@ -0,0 +1,40 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 2 + patch_len: 24 + dropout: 0.3 + f_hidden_size: 16 + n_layers: 3 + n_heads: 4 + fc_dropout: 0.3 + head_dropout: 0 + individual: true + learning_rate: 0.0025 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: illness_ltsf + path: /home/covpreduser/Blob/v-jiawezhang/data/all_datasets/ + split_val: true + scaler: standard # identity, standard, temporal + context_length: 36 + prediction_length: 36 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/illness_ltsf/timegrad.yaml b/config/ltsf/illness_ltsf/timegrad.yaml new file mode 100644 index 0000000..03fe6aa --- /dev/null +++ b/config/ltsf/illness_ltsf/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 200 + enc_hidden_size: 64 + enc_num_layers: 2 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: illness_ltsf + split_val: true + scaler: identity # identity, standard, temporal + context_length: 36 + prediction_length: 36 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/traffic_ltsf/csdi.yaml b/config/ltsf/traffic_ltsf/csdi.yaml new file mode 100644 index 0000000..9fe882f --- /dev/null +++ b/config/ltsf/traffic_ltsf/csdi.yaml @@ -0,0 +1,47 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 1 + check_val_every_n_epoch: 3 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 64 + emb_feature_dim: 8 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 64 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 16 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 4 + test_batch_size: 4 + num_workers: 8 diff --git a/config/ltsf/traffic_ltsf/dlinear.yaml b/config/ltsf/traffic_ltsf/dlinear.yaml new file mode 100644 index 0000000..0c3c79c --- /dev/null +++ b/config/ltsf/traffic_ltsf/dlinear.yaml @@ -0,0 +1,35 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + accumulate_grad_batches: 4 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 25 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.05 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 8 + test_batch_size: 8 + num_workers: 8 diff --git a/config/ltsf/traffic_ltsf/gru_nvp.yaml b/config/ltsf/traffic_ltsf/gru_nvp.yaml new file mode 100644 index 0000000..d4cd530 --- /dev/null +++ b/config/ltsf/traffic_ltsf/gru_nvp.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 128 + enc_num_layers: 3 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 128 + n_hidden: 3 + batch_norm: true + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_ltsf + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/traffic_ltsf/patchtst.yaml b/config/ltsf/traffic_ltsf/patchtst.yaml new file mode 100644 index 0000000..73479f2 --- /dev/null +++ b/config/ltsf/traffic_ltsf/patchtst.yaml @@ -0,0 +1,39 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 300 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 3 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 8 + patch_len: 16 + dropout: 0.2 + f_hidden_size: 128 + n_layers: 3 + n_heads: 16 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 8 + test_batch_size: 8 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/traffic_ltsf/timegrad.yaml b/config/ltsf/traffic_ltsf/timegrad.yaml new file mode 100644 index 0000000..682500a --- /dev/null +++ b/config/ltsf/traffic_ltsf/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 200 + enc_hidden_size: 128 + enc_num_layers: 3 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_ltsf + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/weather_ltsf/csdi.yaml b/config/ltsf/weather_ltsf/csdi.yaml new file mode 100644 index 0000000..0c66d4d --- /dev/null +++ b/config/ltsf/weather_ltsf/csdi.yaml @@ -0,0 +1,47 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 1 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 128 + emb_feature_dim: 16 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 128 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: weather_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 8 + test_batch_size: 8 + num_workers: 8 diff --git a/config/ltsf/weather_ltsf/dlinear.yaml b/config/ltsf/weather_ltsf/dlinear.yaml new file mode 100644 index 0000000..05834b5 --- /dev/null +++ b/config/ltsf/weather_ltsf/dlinear.yaml @@ -0,0 +1,35 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + accumulate_grad_batches: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 25 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: weather_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 diff --git a/config/ltsf/weather_ltsf/gru_nvp.yaml b/config/ltsf/weather_ltsf/gru_nvp.yaml new file mode 100644 index 0000000..0d5a178 --- /dev/null +++ b/config/ltsf/weather_ltsf/gru_nvp.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 64 + enc_num_layers: 4 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 128 + n_hidden: 3 + batch_norm: false + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: weather_ltsf + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/ltsf/weather_ltsf/patchtst.yaml b/config/ltsf/weather_ltsf/patchtst.yaml new file mode 100644 index 0000000..d71cfb5 --- /dev/null +++ b/config/ltsf/weather_ltsf/patchtst.yaml @@ -0,0 +1,39 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 8 + patch_len: 16 + dropout: 0.2 + f_hidden_size: 128 + n_layers: 3 + n_heads: 16 + fc_dropout: 0.2 + head_dropout: 0 + individual: false + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: weather_ltsf + split_val: true + scaler: standard # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/ltsf/weather_ltsf/timegrad.yaml b/config/ltsf/weather_ltsf/timegrad.yaml new file mode 100644 index 0000000..ffcd86a --- /dev/null +++ b/config/ltsf/weather_ltsf/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 200 + enc_hidden_size: 64 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: weather_ltsf + split_val: true + scaler: identity # identity, standard, temporal + context_length: 96 + prediction_length: 96 + batch_size: 16 + test_batch_size: 16 + num_workers: 8 diff --git a/config/m4/m4_daily/csdi.yaml b/config/m4/m4_daily/csdi.yaml new file mode 100644 index 0000000..5e5e202 --- /dev/null +++ b/config/m4/m4_daily/csdi.yaml @@ -0,0 +1,46 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 32 + emb_feature_dim: 4 + channels: 16 + n_layers: 4 + num_heads: 4 + num_steps: 50 + diffusion_embedding_dim: 32 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m4_daily + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/m4_daily/dlinear.yaml b/config/m4/m4_daily/dlinear.yaml new file mode 100644 index 0000000..161b6dd --- /dev/null +++ b/config/m4/m4_daily/dlinear.yaml @@ -0,0 +1,34 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 3 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m4_daily + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/m4_daily/gru_nvp.yaml b/config/m4/m4_daily/gru_nvp.yaml new file mode 100644 index 0000000..09d31a5 --- /dev/null +++ b/config/m4/m4_daily/gru_nvp.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 40 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 2 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 100 + dequantize: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m4_daily + context_length_factor: 3 + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/m4_daily/patchtst.yaml b/config/m4/m4_daily/patchtst.yaml new file mode 100644 index 0000000..e97e8ef --- /dev/null +++ b/config/m4/m4_daily/patchtst.yaml @@ -0,0 +1,39 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 2 + patch_len: 6 + dropout: 0.3 + f_hidden_size: 32 + d_ff: 128 + n_layers: 3 + n_heads: 8 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m4_daily + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 128 + num_workers: 8 \ No newline at end of file diff --git a/config/m4/m4_daily/timegrad.yaml b/config/m4/m4_daily/timegrad.yaml new file mode 100644 index 0000000..376f3c4 --- /dev/null +++ b/config/m4/m4_daily/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 50 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 100 + enc_hidden_size: 64 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m4_daily + context_length_factor: 3 + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/m4_weekly/csdi.yaml b/config/m4/m4_weekly/csdi.yaml new file mode 100644 index 0000000..ad4d95c --- /dev/null +++ b/config/m4/m4_weekly/csdi.yaml @@ -0,0 +1,46 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 32 + emb_feature_dim: 4 + channels: 16 + n_layers: 4 + num_heads: 4 + num_steps: 50 + diffusion_embedding_dim: 32 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m4_weekly + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/m4_weekly/dlinear.yaml b/config/m4/m4_weekly/dlinear.yaml new file mode 100644 index 0000000..3b3b918 --- /dev/null +++ b/config/m4/m4_weekly/dlinear.yaml @@ -0,0 +1,34 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 3 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m4_weekly + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/m4_weekly/gru_nvp.yaml b/config/m4/m4_weekly/gru_nvp.yaml new file mode 100644 index 0000000..2f372c0 --- /dev/null +++ b/config/m4/m4_weekly/gru_nvp.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 40 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 2 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 100 + dequantize: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m4_weekly + context_length_factor: 3 + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/m4_weekly/patchtst.yaml b/config/m4/m4_weekly/patchtst.yaml new file mode 100644 index 0000000..2c63319 --- /dev/null +++ b/config/m4/m4_weekly/patchtst.yaml @@ -0,0 +1,39 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 3 + patch_len: 6 + dropout: 0.3 + f_hidden_size: 32 + d_ff: 128 + n_layers: 3 + n_heads: 8 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m4_weekly + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 128 + num_workers: 8 \ No newline at end of file diff --git a/config/m4/m4_weekly/timegrad.yaml b/config/m4/m4_weekly/timegrad.yaml new file mode 100644 index 0000000..43e873c --- /dev/null +++ b/config/m4/m4_weekly/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 50 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 100 + enc_hidden_size: 64 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m4_weekly + context_length_factor: 3 + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/m5/csdi.yaml b/config/m4/m5/csdi.yaml new file mode 100644 index 0000000..aa8dee7 --- /dev/null +++ b/config/m4/m5/csdi.yaml @@ -0,0 +1,46 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 32 + emb_feature_dim: 4 + channels: 16 + n_layers: 4 + num_heads: 4 + num_steps: 50 + diffusion_embedding_dim: 32 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m5 + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/m5/dlinear.yaml b/config/m4/m5/dlinear.yaml new file mode 100644 index 0000000..848f9a0 --- /dev/null +++ b/config/m4/m5/dlinear.yaml @@ -0,0 +1,35 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 3 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m5 + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 256 + num_workers: 8 diff --git a/config/m4/m5/gru_nvp.yaml b/config/m4/m5/gru_nvp.yaml new file mode 100644 index 0000000..71a8a9a --- /dev/null +++ b/config/m4/m5/gru_nvp.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 40 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 2 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 100 + dequantize: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m5 + context_length_factor: 3 + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/m5/patchtst.yaml b/config/m4/m5/patchtst.yaml new file mode 100644 index 0000000..6fbe3b1 --- /dev/null +++ b/config/m4/m5/patchtst.yaml @@ -0,0 +1,40 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 2 + patch_len: 4 + dropout: 0.3 + f_hidden_size: 64 + d_ff: 128 + n_layers: 3 + n_heads: 8 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m5 + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 128 + num_workers: 8 \ No newline at end of file diff --git a/config/m4/m5/timegrad.yaml b/config/m4/m5/timegrad.yaml new file mode 100644 index 0000000..2437872 --- /dev/null +++ b/config/m4/m5/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 30 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 50 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 100 + enc_hidden_size: 64 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: m5 + context_length_factor: 3 + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 1 + test_batch_size: 512 + num_workers: 8 diff --git a/config/m4/tourism_monthly/csdi.yaml b/config/m4/tourism_monthly/csdi.yaml new file mode 100644 index 0000000..ed49c8e --- /dev/null +++ b/config/m4/tourism_monthly/csdi.yaml @@ -0,0 +1,46 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 32 + emb_feature_dim: 4 + channels: 16 + n_layers: 4 + num_heads: 4 + num_steps: 50 + diffusion_embedding_dim: 32 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 64 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: tourism_monthly + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/tourism_monthly/dlinear.yaml b/config/m4/tourism_monthly/dlinear.yaml new file mode 100644 index 0000000..4181943 --- /dev/null +++ b/config/m4/tourism_monthly/dlinear.yaml @@ -0,0 +1,34 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 3 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: tourism_monthly + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/tourism_monthly/gru_nvp.yaml b/config/m4/tourism_monthly/gru_nvp.yaml new file mode 100644 index 0000000..fae0350 --- /dev/null +++ b/config/m4/tourism_monthly/gru_nvp.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 40 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 2 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 100 + dequantize: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: tourism_monthly + context_length_factor: 3 + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/m4/tourism_monthly/patchtst.yaml b/config/m4/tourism_monthly/patchtst.yaml new file mode 100644 index 0000000..de72060 --- /dev/null +++ b/config/m4/tourism_monthly/patchtst.yaml @@ -0,0 +1,39 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 2 + patch_len: 6 + dropout: 0.3 + f_hidden_size: 64 + d_ff: 128 + n_layers: 3 + n_heads: 8 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: tourism_monthly + context_length_factor: 3 + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 1 + test_batch_size: 128 + num_workers: 8 \ No newline at end of file diff --git a/config/m4/tourism_monthly/timegrad.yaml b/config/m4/tourism_monthly/timegrad.yaml new file mode 100644 index 0000000..848e726 --- /dev/null +++ b/config/m4/tourism_monthly/timegrad.yaml @@ -0,0 +1,44 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 800 + log_every_n_steps: 2 + check_val_every_n_epoch: 2 + default_root_dir: ./results + accumulate_grad_batches: 8 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 50 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 100 + enc_hidden_size: 64 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: tourism_monthly + context_length_factor: 3 + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 diff --git a/config/pipeline_config.yaml b/config/pipeline_config.yaml new file mode 100644 index 0000000..2dcdd3e --- /dev/null +++ b/config/pipeline_config.yaml @@ -0,0 +1,53 @@ +# lightning.pytorch==2.3.0dev +seed_everything: true +trainer: + accelerator: auto + strategy: auto + devices: auto + num_nodes: 1 + precision: null + logger: null + callbacks: null + fast_dev_run: false + max_epochs: null + min_epochs: null + max_steps: -1 + min_steps: null + max_time: null + limit_train_batches: null + limit_val_batches: null + limit_test_batches: null + limit_predict_batches: null + overfit_batches: 0.0 + val_check_interval: null + check_val_every_n_epoch: 1 + num_sanity_val_steps: null + log_every_n_steps: null + enable_checkpointing: null + enable_progress_bar: null + enable_model_summary: null + accumulate_grad_batches: 1 + gradient_clip_val: null + gradient_clip_algorithm: null + deterministic: null + benchmark: null + inference_mode: true + use_distributed_sampler: true + profiler: null + detect_anomaly: false + barebones: false + plugins: null + sync_batchnorm: false + reload_dataloaders_every_n_epochs: 0 + default_root_dir: null +model: + forecaster: null + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 10 + load_from_ckpt: null +data: + data_manager: null + batch_size: 64 + test_batch_size: 8 + num_workers: 8 diff --git a/config/stsf/electricity/dlinear.yaml b/config/stsf/electricity/dlinear.yaml new file mode 100644 index 0000000..4e60590 --- /dev/null +++ b/config/stsf/electricity/dlinear.yaml @@ -0,0 +1,32 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: true + kernel_size: 3 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.01 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/electricity/gru.yaml b/config/stsf/electricity/gru.yaml new file mode 100644 index 0000000..24c9072 --- /dev/null +++ b/config/stsf/electricity/gru.yaml @@ -0,0 +1,34 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.GRUForecaster + init_args: + f_hidden_size: 40 + num_layers: 2 + dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/electricity/gru_maf.yaml b/config/stsf/electricity/gru_maf.yaml new file mode 100644 index 0000000..c39a2bf --- /dev/null +++ b/config/stsf/electricity/gru_maf.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_MAF + init_args: + enc_num_layers: 2 + enc_hidden_size: 40 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/electricity/gru_nvp.yaml b/config/stsf/electricity/gru_nvp.yaml new file mode 100644 index 0000000..ad2c480 --- /dev/null +++ b/config/stsf/electricity/gru_nvp.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 40 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 3 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/electricity/patchtst.yaml b/config/stsf/electricity/patchtst.yaml new file mode 100644 index 0000000..5a1d4d8 --- /dev/null +++ b/config/stsf/electricity/patchtst.yaml @@ -0,0 +1,37 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 2 + patch_len: 4 + dropout: 0.1 + f_hidden_size: 64 + n_layers: 4 + n_heads: 8 + fc_dropout: 0.1 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/electricity/timegrad.yaml b/config/stsf/electricity/timegrad.yaml new file mode 100644 index 0000000..aaecf63 --- /dev/null +++ b/config/stsf/electricity/timegrad.yaml @@ -0,0 +1,41 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 100 + enc_hidden_size: 128 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/electricity/timesnet.yaml b/config/stsf/electricity/timesnet.yaml new file mode 100644 index 0000000..e5e0a62 --- /dev/null +++ b/config/stsf/electricity/timesnet.yaml @@ -0,0 +1,36 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TimesNet + init_args: + n_layers: 2 + num_kernels: 6 + top_k: 5 + d_ff: 64 + dropout: 0.1 + f_hidden_size: 64 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/electricity/trans_maf.yaml b/config/stsf/electricity/trans_maf.yaml new file mode 100644 index 0000000..1c61bb0 --- /dev/null +++ b/config/stsf/electricity/trans_maf.yaml @@ -0,0 +1,46 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.Trans_MAF + init_args: + enc_hidden_size: 32 + enc_num_heads: 8 + enc_num_encoder_layers: 2 + enc_num_decoder_layers: 2 + enc_dim_feedforward_scale: 4 + enc_dropout: 0.1 + enc_activation: gelu + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/electricity/transformer.yaml b/config/stsf/electricity/transformer.yaml new file mode 100644 index 0000000..667ad85 --- /dev/null +++ b/config/stsf/electricity/transformer.yaml @@ -0,0 +1,38 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TransformerForecaster + init_args: + f_hidden_size: 32 + num_heads: 8 + num_encoder_layers: 3 + num_decoder_layers: 3 + dim_feedforward_scale: 4 + dropout: 0.1 + activation: gelu + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: electricity_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/exchange/dlinear.yaml b/config/stsf/exchange/dlinear.yaml new file mode 100644 index 0000000..56eccc3 --- /dev/null +++ b/config/stsf/exchange/dlinear.yaml @@ -0,0 +1,32 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 3 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.01 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_rate_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/exchange/gru.yaml b/config/stsf/exchange/gru.yaml new file mode 100644 index 0000000..411cb52 --- /dev/null +++ b/config/stsf/exchange/gru.yaml @@ -0,0 +1,34 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.GRUForecaster + init_args: + f_hidden_size: 40 + num_layers: 2 + dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_rate_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/exchange/gru_maf.yaml b/config/stsf/exchange/gru_maf.yaml new file mode 100644 index 0000000..c30ef96 --- /dev/null +++ b/config/stsf/exchange/gru_maf.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_MAF + init_args: + enc_num_layers: 2 + enc_hidden_size: 40 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: false + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_rate_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/exchange/gru_nvp.yaml b/config/stsf/exchange/gru_nvp.yaml new file mode 100644 index 0000000..4b05825 --- /dev/null +++ b/config/stsf/exchange/gru_nvp.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 40 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_rate_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/exchange/patchtst.yaml b/config/stsf/exchange/patchtst.yaml new file mode 100644 index 0000000..f773b9d --- /dev/null +++ b/config/stsf/exchange/patchtst.yaml @@ -0,0 +1,37 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 3 + patch_len: 6 + dropout: 0.1 + f_hidden_size: 32 + n_layers: 3 + n_heads: 8 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_rate_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/exchange/timegrad.yaml b/config/stsf/exchange/timegrad.yaml new file mode 100644 index 0000000..554e59b --- /dev/null +++ b/config/stsf/exchange/timegrad.yaml @@ -0,0 +1,41 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 100 + enc_hidden_size: 128 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_rate_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/exchange/timesnet.yaml b/config/stsf/exchange/timesnet.yaml new file mode 100644 index 0000000..2e7838c --- /dev/null +++ b/config/stsf/exchange/timesnet.yaml @@ -0,0 +1,36 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TimesNet + init_args: + n_layers: 2 + num_kernels: 6 + top_k: 5 + d_ff: 64 + dropout: 0.1 + f_hidden_size: 64 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_rate_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/exchange/trans_maf.yaml b/config/stsf/exchange/trans_maf.yaml new file mode 100644 index 0000000..e39513e --- /dev/null +++ b/config/stsf/exchange/trans_maf.yaml @@ -0,0 +1,46 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.Trans_MAF + init_args: + enc_hidden_size: 16 + enc_num_heads: 8 + enc_num_encoder_layers: 2 + enc_num_decoder_layers: 2 + enc_dim_feedforward_scale: 4 + enc_dropout: 0.1 + enc_activation: gelu + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: false + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_rate_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/exchange/transformer.yaml b/config/stsf/exchange/transformer.yaml new file mode 100644 index 0000000..1d4ab67 --- /dev/null +++ b/config/stsf/exchange/transformer.yaml @@ -0,0 +1,38 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TransformerForecaster + init_args: + f_hidden_size: 32 + num_heads: 8 + num_encoder_layers: 3 + num_decoder_layers: 3 + dim_feedforward_scale: 4 + dropout: 0.1 + activation: gelu + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: exchange_rate_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/solar/dlinear.yaml b/config/stsf/solar/dlinear.yaml new file mode 100644 index 0000000..3bd35fe --- /dev/null +++ b/config/stsf/solar/dlinear.yaml @@ -0,0 +1,32 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 3 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + learning_rate: 0.01 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/solar/gru.yaml b/config/stsf/solar/gru.yaml new file mode 100644 index 0000000..d1cf880 --- /dev/null +++ b/config/stsf/solar/gru.yaml @@ -0,0 +1,34 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.GRUForecaster + init_args: + f_hidden_size: 40 + num_layers: 2 + dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/solar/gru_maf.yaml b/config/stsf/solar/gru_maf.yaml new file mode 100644 index 0000000..e974359 --- /dev/null +++ b/config/stsf/solar/gru_maf.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_MAF + init_args: + enc_num_layers: 2 + enc_hidden_size: 40 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: false + conditional_length: 200 + dequantize: true + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/solar/gru_nvp.yaml b/config/stsf/solar/gru_nvp.yaml new file mode 100644 index 0000000..7d3f8c3 --- /dev/null +++ b/config/stsf/solar/gru_nvp.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 40 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: true + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/solar/patchtst.yaml b/config/stsf/solar/patchtst.yaml new file mode 100644 index 0000000..e190df7 --- /dev/null +++ b/config/stsf/solar/patchtst.yaml @@ -0,0 +1,37 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 3 + patch_len: 6 + dropout: 0.1 + f_hidden_size: 32 + n_layers: 3 + n_heads: 8 + fc_dropout: 0.2 + head_dropout: 0 + individual: true + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/solar/timegrad.yaml b/config/stsf/solar/timegrad.yaml new file mode 100644 index 0000000..9681ae0 --- /dev/null +++ b/config/stsf/solar/timegrad.yaml @@ -0,0 +1,41 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 100 + enc_hidden_size: 128 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/solar/timesnet.yaml b/config/stsf/solar/timesnet.yaml new file mode 100644 index 0000000..f23eabc --- /dev/null +++ b/config/stsf/solar/timesnet.yaml @@ -0,0 +1,36 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TimesNet + init_args: + n_layers: 2 + num_kernels: 6 + top_k: 5 + d_ff: 16 + dropout: 0.1 + f_hidden_size: 16 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/solar/trans_maf.yaml b/config/stsf/solar/trans_maf.yaml new file mode 100644 index 0000000..191c620 --- /dev/null +++ b/config/stsf/solar/trans_maf.yaml @@ -0,0 +1,46 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.Trans_MAF + init_args: + enc_hidden_size: 32 + enc_num_heads: 8 + enc_num_encoder_layers: 2 + enc_num_decoder_layers: 2 + enc_dim_feedforward_scale: 4 + enc_dropout: 0.1 + enc_activation: gelu + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: false + conditional_length: 200 + dequantize: true + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/solar/transformer.yaml b/config/stsf/solar/transformer.yaml new file mode 100644 index 0000000..00d1fef --- /dev/null +++ b/config/stsf/solar/transformer.yaml @@ -0,0 +1,38 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TransformerForecaster + init_args: + f_hidden_size: 16 + num_heads: 4 + num_encoder_layers: 3 + num_decoder_layers: 3 + dim_feedforward_scale: 4 + dropout: 0.1 + activation: gelu + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/traffic/csdi.yaml b/config/stsf/traffic/csdi.yaml new file mode 100644 index 0000000..8451175 --- /dev/null +++ b/config/stsf/traffic/csdi.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + check_val_every_n_epoch: 3 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 64 + emb_feature_dim: 8 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 64 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 16 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 8 + test_batch_size: 8 + num_workers: 8 diff --git a/config/stsf/traffic/dlinear.yaml b/config/stsf/traffic/dlinear.yaml new file mode 100644 index 0000000..6bd4524 --- /dev/null +++ b/config/stsf/traffic/dlinear.yaml @@ -0,0 +1,32 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 3 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/traffic/gru.yaml b/config/stsf/traffic/gru.yaml new file mode 100644 index 0000000..8613675 --- /dev/null +++ b/config/stsf/traffic/gru.yaml @@ -0,0 +1,34 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.GRUForecaster + init_args: + f_hidden_size: 128 + num_layers: 2 + dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 diff --git a/config/stsf/traffic/gru_maf.yaml b/config/stsf/traffic/gru_maf.yaml new file mode 100644 index 0000000..ea9c941 --- /dev/null +++ b/config/stsf/traffic/gru_maf.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_MAF + init_args: + enc_num_layers: 2 + enc_hidden_size: 128 + enc_dropout: 0.3 + n_blocks: 3 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/traffic/gru_nvp.yaml b/config/stsf/traffic/gru_nvp.yaml new file mode 100644 index 0000000..eef657c --- /dev/null +++ b/config/stsf/traffic/gru_nvp.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 128 + enc_num_layers: 2 + enc_dropout: 0.3 + n_blocks: 4 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/traffic/patchtst.yaml b/config/stsf/traffic/patchtst.yaml new file mode 100644 index 0000000..7790448 --- /dev/null +++ b/config/stsf/traffic/patchtst.yaml @@ -0,0 +1,38 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 1 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 3 + patch_len: 6 + dropout: 0.1 + f_hidden_size: 32 + n_layers: 3 + n_heads: 8 + fc_dropout: 0.2 + head_dropout: 0 + individual: false + num_samples: 100 + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/traffic/timegrad.yaml b/config/stsf/traffic/timegrad.yaml new file mode 100644 index 0000000..36caadf --- /dev/null +++ b/config/stsf/traffic/timegrad.yaml @@ -0,0 +1,41 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 100 + enc_hidden_size: 128 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 diff --git a/config/stsf/traffic/timesnet.yaml b/config/stsf/traffic/timesnet.yaml new file mode 100644 index 0000000..96b0cca --- /dev/null +++ b/config/stsf/traffic/timesnet.yaml @@ -0,0 +1,36 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TimesNet + init_args: + n_layers: 2 + num_kernels: 6 + top_k: 5 + d_ff: 16 + dropout: 0.1 + f_hidden_size: 16 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/traffic/trans_maf.yaml b/config/stsf/traffic/trans_maf.yaml new file mode 100644 index 0000000..0ffb6fd --- /dev/null +++ b/config/stsf/traffic/trans_maf.yaml @@ -0,0 +1,46 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.Trans_MAF + init_args: + enc_hidden_size: 128 + enc_num_heads: 4 + enc_num_encoder_layers: 2 + enc_num_decoder_layers: 2 + enc_dim_feedforward_scale: 4 + enc_dropout: 0.1 + enc_activation: gelu + n_blocks: 3 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: false + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/traffic/transformer.yaml b/config/stsf/traffic/transformer.yaml new file mode 100644 index 0000000..cf89ac0 --- /dev/null +++ b/config/stsf/traffic/transformer.yaml @@ -0,0 +1,38 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TransformerForecaster + init_args: + f_hidden_size: 32 + num_heads: 8 + num_encoder_layers: 3 + num_decoder_layers: 3 + dim_feedforward_scale: 4 + dropout: 0.1 + activation: gelu + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: traffic_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/wiki/csdi.yaml b/config/stsf/wiki/csdi.yaml new file mode 100644 index 0000000..861f4b2 --- /dev/null +++ b/config/stsf/wiki/csdi.yaml @@ -0,0 +1,45 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + check_val_every_n_epoch: 3 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.CSDI + init_args: + emb_time_dim: 64 + emb_feature_dim: 8 + channels: 64 + n_layers: 4 + num_heads: 8 + num_steps: 50 + diffusion_embedding_dim: 64 + beta_start: 0.001 + beta_end: 0.5 + sample_size: 16 + linear_trans: false + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + feat_idx_emb_dim: 1 + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: wiki2000_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 8 + test_batch_size: 8 + num_workers: 8 diff --git a/config/stsf/wiki/dlinear.yaml b/config/stsf/wiki/dlinear.yaml new file mode 100644 index 0000000..9de36cc --- /dev/null +++ b/config/stsf/wiki/dlinear.yaml @@ -0,0 +1,32 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.DLinear + init_args: + individual: false + kernel_size: 3 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: wiki2000_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 32 + test_batch_size: 32 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/wiki/gru.yaml b/config/stsf/wiki/gru.yaml new file mode 100644 index 0000000..9050ba2 --- /dev/null +++ b/config/stsf/wiki/gru.yaml @@ -0,0 +1,34 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.GRUForecaster + init_args: + f_hidden_size: 40 + num_layers: 2 + dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: wiki2000_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/wiki/gru_maf.yaml b/config/stsf/wiki/gru_maf.yaml new file mode 100644 index 0000000..14a509e --- /dev/null +++ b/config/stsf/wiki/gru_maf.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_MAF + init_args: + enc_num_layers: 2 + enc_hidden_size: 40 + enc_dropout: 0.1 + n_blocks: 3 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: true + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: wiki2000_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/wiki/gru_nvp.yaml b/config/stsf/wiki/gru_nvp.yaml new file mode 100644 index 0000000..968a739 --- /dev/null +++ b/config/stsf/wiki/gru_nvp.yaml @@ -0,0 +1,42 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.GRU_NVP + init_args: + enc_hidden_size: 40 + enc_num_layers: 2 + enc_dropout: 0.1 + n_blocks: 3 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: true + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: wiki2000_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/wiki/patchtst.yaml b/config/stsf/wiki/patchtst.yaml new file mode 100644 index 0000000..0eb87a9 --- /dev/null +++ b/config/stsf/wiki/patchtst.yaml @@ -0,0 +1,38 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 400 + log_every_n_steps: 1 + default_root_dir: ./results + accumulate_grad_batches: 4 +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.PatchTST + init_args: + stride: 4 + patch_len: 8 + dropout: 0.1 + f_hidden_size: 32 + n_layers: 2 + n_heads: 8 + fc_dropout: 0.2 + head_dropout: 0 + individual: false + num_samples: 100 + learning_rate: 0.0001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: wiki2000_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 16 + test_batch_size: 16 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/wiki/timegrad.yaml b/config/stsf/wiki/timegrad.yaml new file mode 100644 index 0000000..ff8a685 --- /dev/null +++ b/config/stsf/wiki/timegrad.yaml @@ -0,0 +1,41 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.TimeGrad + init_args: + loss_type: l2 + diff_steps: 100 + beta_end: 0.1 + beta_schedule: linear + conditional_length: 100 + enc_hidden_size: 128 + enc_num_layers: 4 + enc_dropout: 0.1 + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: wiki2000_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/wiki/timesnet.yaml b/config/stsf/wiki/timesnet.yaml new file mode 100644 index 0000000..78bc757 --- /dev/null +++ b/config/stsf/wiki/timesnet.yaml @@ -0,0 +1,36 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TimesNet + init_args: + n_layers: 2 + num_kernels: 6 + top_k: 5 + d_ff: 32 + dropout: 0.1 + f_hidden_size: 32 + use_lags: false + use_feat_idx_emb: false + use_time_feat: false + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: wiki2000_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 diff --git a/config/stsf/wiki/trans_maf.yaml b/config/stsf/wiki/trans_maf.yaml new file mode 100644 index 0000000..2f5afd5 --- /dev/null +++ b/config/stsf/wiki/trans_maf.yaml @@ -0,0 +1,46 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.Trans_MAF + init_args: + enc_hidden_size: 128 + enc_num_heads: 4 + enc_num_encoder_layers: 2 + enc_num_decoder_layers: 2 + enc_dim_feedforward_scale: 4 + enc_dropout: 0.1 + enc_activation: gelu + n_blocks: 3 + hidden_size: 100 + n_hidden: 2 + batch_norm: true + conditional_length: 200 + dequantize: true + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + use_scaling: true + num_samples: 100 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: wiki2000_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/stsf/wiki/transformer.yaml b/config/stsf/wiki/transformer.yaml new file mode 100644 index 0000000..29f6958 --- /dev/null +++ b/config/stsf/wiki/transformer.yaml @@ -0,0 +1,38 @@ +# lightning==2.3.0.dev0 +seed_everything: 1 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 50 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TransformerForecaster + init_args: + f_hidden_size: 32 + num_heads: 8 + num_encoder_layers: 3 + num_decoder_layers: 3 + dim_feedforward_scale: 4 + dropout: 0.1 + activation: gelu + use_lags: true + use_feat_idx_emb: true + use_time_feat: true + feat_idx_emb_dim: 1 + learning_rate: 0.001 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: wiki2000_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/chronos.yaml b/config/tsfm/chronos.yaml new file mode 100644 index 0000000..f43a052 --- /dev/null +++ b/config/tsfm/chronos.yaml @@ -0,0 +1,28 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.Chronos + init_args: + model_size: base + num_samples: 100 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 1 + test_batch_size: 1 + num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/forecastpfn.yaml b/config/tsfm/forecastpfn.yaml new file mode 100644 index 0000000..974ca5d --- /dev/null +++ b/config/tsfm/forecastpfn.yaml @@ -0,0 +1,29 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.ForecastPFN + init_args: + label_len: 48 + ckpt_path: /path/to/checkpoints/ForecastPFN/saved_weights + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + timeenc: 2 # TODO: fix bug + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/lag_llama.yaml b/config/tsfm/lag_llama.yaml new file mode 100644 index 0000000..c87a357 --- /dev/null +++ b/config/tsfm/lag_llama.yaml @@ -0,0 +1,29 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.LagLlama + init_args: + use_rope_scaling: true + num_samples: 100 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: identity # identity, standard, temporal + timeenc: 2 + batch_size: 1 + test_batch_size: 1 + num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/moirai.yaml b/config/tsfm/moirai.yaml new file mode 100644 index 0000000..86f273a --- /dev/null +++ b/config/tsfm/moirai.yaml @@ -0,0 +1,31 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.prob_forecaster.Moirai + init_args: + variate_mode: S + patch_size: auto + model_size: base + scaling: true + num_samples: 100 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: identity # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/timer.yaml b/config/tsfm/timer.yaml new file mode 100644 index 0000000..97f051f --- /dev/null +++ b/config/tsfm/timer.yaml @@ -0,0 +1,28 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.Timer + init_args: + label_len: 96 + ckpt_path: /path/to/checkpoints/timer/Timer_67M_UTSD_4G.pt + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/timesfm.yaml b/config/tsfm/timesfm.yaml new file mode 100644 index 0000000..b769246 --- /dev/null +++ b/config/tsfm/timesfm.yaml @@ -0,0 +1,31 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TimesFM + init_args: + input_patch_len: 32 + output_patch_len: 128 + num_layers: 20 + model_dims: 1280 + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + # var_norm: true + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/tinytimemixer.yaml b/config/tsfm/tinytimemixer.yaml new file mode 100644 index 0000000..6ba051a --- /dev/null +++ b/config/tsfm/tinytimemixer.yaml @@ -0,0 +1,25 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.TinyTimeMixer + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/config/tsfm/units.yaml b/config/tsfm/units.yaml new file mode 100644 index 0000000..056ac80 --- /dev/null +++ b/config/tsfm/units.yaml @@ -0,0 +1,28 @@ +# lightning==2.3.0.dev0 +seed_everything: 0 +trainer: + accelerator: gpu + devices: 1 + strategy: auto + max_epochs: 40 + use_distributed_sampler: false + limit_train_batches: 100 + log_every_n_steps: 1 + default_root_dir: ./results +model: + forecaster: + class_path: probts.model.forecaster.point_forecaster.UniTS + init_args: + ckpt_path: /path/to/checkpoints/units/units_x128_pretrain_checkpoint.pth + quantiles_num: 20 +data: + data_manager: + class_path: probts.data.data_manager.DataManager + init_args: + dataset: solar_nips + split_val: true + scaler: standard # identity, standard, temporal + # var_norm: true + batch_size: 64 + test_batch_size: 64 + num_workers: 8 \ No newline at end of file diff --git a/docs/benchmark/FOUNDATION_MODEL.md b/docs/benchmark/FOUNDATION_MODEL.md new file mode 100644 index 0000000..e489e16 --- /dev/null +++ b/docs/benchmark/FOUNDATION_MODEL.md @@ -0,0 +1,32 @@ +## Time Series Foundation Models Benchmarking + + + + +We have incorporated eight recently emerged time series foundation models, namely [Lag-Llama](https://github.com/time-series-foundation-models/lag-llama), [Chronos](https://github.com/amazon-science/chronos-forecasting), [TimesFM](https://github.com/google-research/timesfm), [Timer](https://github.com/thuml/Large-Time-Series-Model), [MOIRAI](https://github.com/SalesforceAIResearch/uni2ts), [UniTS](https://github.com/mims-harvard/UniTS), [ForecastPFN](https://github.com/abacusai/ForecastPFN), and [TTM](https://github.com/ibm-granite/granite-tsfm), into our framework. These foundation models are categorized based on their capabilities, such as zero-shot forecasting, adaptability to varying prediction lengths, and support for probabilistic predictions, as well as their architectural designs, including whether they are auto-regressive and the nature of their backbone networks. Additionally, we have detailed their training processes, including the lengths of prediction horizons used during pre-training and the sizes of look-back windows. + +Detailed configuration files can be found in folder [config/tsfm/](../../config/tsfm/). + +### A Comparison of Pre-trained Time-series Foundation Models + +Table 1. Foundation Models for Time Series. **Zero-shot** indicates whether the original work tests zero-shot capabilities. **Any-horizon** indicates if the same pre-trained model can adapt to prediction tasks of varying lengths. **AR** denotes if the model performs auto-regressive forecasting. **Prob.** indicates if the model natively supports probabilistic forecasting. **Arch.** denotes the model's backbone architecture: D-O for decoder-only transformer, E-O for encoder-only transformer, E-D for encoder-decoder transformer, and unique for specially designed backbones. **Multi-variate** indicates if the model explicitly handles multivariate relationships. **Pre-train Horizons** specifies the forecasting task horizons during pre-training. **Look-back Window** specifies the context history length settings used in the original experiments. + +![Foundation model summary](./figs/FM_summary.jpg) + +Table 2. Evaluation Datasets for Time-series Foundation Models. We selected several popular datasets to evaluate time-series foundation models. \checkmark indicates pre-training on the dataset, $\bigcirc$ indicates zero-shot evaluation on the dataset, few indicates few-shot evaluation on the dataset, and ✗ indicates the dataset is not mentioned in the paper or documentation. ‘*’ indicates that the data comes from the same source but may be processed differently. + + +![pre-train datasets summary](./figs/FM_dataset.jpg) + + +### Comparison of Time-series Foundation Models on Diverse Prediction Horizons + +Table 3. NMAE of time-series foundation models on diverse prediction horizons. The input sequence length is set to 96 if not specified. For every model, we exclude the evaluation results on its pre-trained datasets + +![Comparison of Time-series Foundation Models on Diverse Prediction Horizons](./figs/fm_var_hor.jpg) + +### Comparison of Time-series Foundation Models on Short-term Probabilistic Forecasting + +Table 4. Results of probabilistic foundation models on short-term distributional forecasting. For every model, we exclude the evaluation results on its pre-trained datasets. + +![Comparison of Time-series Foundation Models on short-term scenerio](./figs/fm_short_term.jpg) diff --git a/docs/benchmark/README.md b/docs/benchmark/README.md new file mode 100644 index 0000000..76ca2a8 --- /dev/null +++ b/docs/benchmark/README.md @@ -0,0 +1,23 @@ +# Benchmarking :balance_scale: + +We conducted a comprehensive benchmarking and analysis of a diverse range of state-of-the-art models from different strands of research. We mainly assessed these models using NAME and CRPS metrics across multiple forecasting horizons, repeating each experiment five times with different seeds to ensure result reliability. + +Results of **time series foundation models** see [HERE](./FOUNDATION_MODEL.md). + +## Long-term Forecasting Benchmarking + +Detailed configuration files can be found in folder [config/ltsf/](../../config/ltsf/). + +Table 1. Results ($\textrm{mean}_{\textrm{std}}$) on long-term forecasting scenarios with the best in $\textbf{bold}$ and the second $\underline{\textrm{underlined}}$, each containing five independent runs with different seeds. The input sequence length is set to 36 for the ILI-L dataset and 96 for the others. Due to the excessive time and memory consumption of CSDI in producing long-term forecasts, its results are unavailable in some datasets. + +![long-term forecasting experimental results](./figs/long_bench.jpg) + + + +## Short-term Forecasting Benchmarking + +Detailed configuration files can be found in folder [config/stsf/](../../config/stsf/). + +Table 2.Results ($\textrm{mean}_{\textrm{std}}$) on short-term forecasting scenarios with the best in $\textbf{bold}$ and the second $\underline{\textrm{underlined}}$, each containing five independent runs with different seeds. + +![short-term forecasting experimental results](./figs/short_bench.jpg) diff --git a/docs/benchmark/figs/FM_dataset.jpg b/docs/benchmark/figs/FM_dataset.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6e2d8c751237195ea92bdd996b7cbdfbaadfacc5 GIT binary patch literal 268047 zcmeEvcT`i`*6*fCH*};1DWVjSCa6e2q=^_oArvJ_ktRkFBuY$>-a`>Mf&zkIp-C4+ zB3(cQL0Ui{38GRHz!*&kZ+p(Y-+jkB#=Gx(3E2L}Myfd7Ck5@2SD_P+oC_V$1V004XdN1Q*v4feo809mjB0B0fRKlcc< z6>|Nv&pGn*!!0tWnXQEd@}wi&%J#VBpQq;BXMXgEC8r1g1YHUVcC#+}_5;I0E`$3y?t*;@&%i(XPk;7<5EF zfX8h7Jl^-uJ}2MLzUN%soMej!SF`}XZqGWSM#`Y4^cel^4= z=&F+KB`;L)WmNF_5Fc;w*uSpt=eGdSzYhF)$9C%;IAFZz&%OU2fBc+>zfQOA@aO&I zkb3>Eeg7KBD>V4pKi+Y0>I1-kdjGF;+}!~H@Dc#vX8miQK?(pAY5@Qw>tFkJ=>mX= z4gj>?-FgX#@^Ov`v~h7L1Dv89T%sIXod6WP)jS-3ZvVV24o)s^o^8B*`~rgD0resP zCkGc7CpQ-l4>zc=95=zw0d7$qv7Nf++r%Bt^D1AF(7Tm>pHJmTb(^H)JF2R_XK*CH zfRywO8CkVm>bv)77#Qw1GCpw7;;5ySwTUGr5#S|ydHY=O_45z78WMUf41N7Z zRCG*iTzo=eM&|9T?3_RDza;xbE>Tb}PHt{4Zr-1Aad3u# zjZ2i9XQ%EqF>?pr^H;=`^=|P=97(@l-Nvt???{#O41OmdrD{M@qx}@^Ps#r81dIHC zlI*_(`2F*O`rhnY&SU}?y00hy1sfEbP~famfSP=Hs&kxtIA?wLVH&PuYL;Mya$fXHXKfgU`J`Uu57 zQOuU$Wl46hu_o8SZ&~2hLIIpp2x9FqI4T!gfV2g`6iv2(_WOkI2l2nJ_4j@KtpmSx z;I|I^)`8zT@LLCd>%eav_^kuKb>O!S{MLcrI`CTue(S(*9r&#SzjffZ4*b@E-#YMH z2Y&0oZyorp1OM}N;QJP^1#pw7xMj)Nsz}-SET+9_IeT|bCqmy{;R0*H2zoB?kR|?N zscSVfw}xaSq|hTj$BsD3VJUHvcWV%=ize;{ez)KE;D3+~aBaQYWR)XD8F+Il_SPfx z!!9H0mm5}DTvH#;o-t0{eLne;?==&EC&>9yb+NzV&Y_jM!`NgX-csLa;qsz=ksmwgub@boY?otS0ynqyvB8VpFSwnL!~ zzp2Yky5xCFSN2d|#zQ}V6s(_tlL>hNcNnac~ro>beF1FIdhX!%4r?M0L-aTjx%it?=&Wcpd2(J|ZszBO7?B`AV7Vx=(f!hK&M+w@5 z*{4wPuLFwcOpDDt6OrRIv=_XIhKk?c*t;WEntAY*^^tVcMa3O&q%nx~Z)VWRPq$zJ zRTBf`u!Lod40Q`2E2dX1<`h{)z8#viHHcKzJK&^ntti{Hy)~jLh2RaHh?}iyn~7%g zjIwk{3I}y0?23tJJ_PCn$>-kAk-pY9#(j35-V@zx9FmbS#$1wgdqBd$^3Qg?pmQL3 zMxa&L#WesYX>#g3Dg3OJRqVuSsY~>b@`}&#waW*LzjWV7qzy1G;m5ZCcH&Ij7C_Yw zoW@+Gj*(_MKO8W?$4~DVHi7#{QRLn3-8J%;xf1IwR(8?zP$lFAPOJdhqFc;Rrw}@E z+t4?lwO`zL6AV*KhqSWSmX}d#iE6#7ca?8tTm|KBj5N7Qf+Uzq*U+jWz6Rb5JMzAz zBj|bn+gtCuqXE1=?rdf9!ztN?#aR?XZ3|%iLFj;f3&m|7@<7ZO01lQC9P5PSEg%p% z@sRW>0o5W$K_$rUO{w@&{`ji@m5?$mtp#~rkDfp-{Efz09bz8+8l#x(j^o9cFrpry zquiTa6*Ujp-BNv9y(es^wa-agdR1=$LmSp!0Qh4Csi>|L^`rGqX_o97x^!a$P0Mlm z2z@|_X|Y=|aA!RCg~EQu{}PP~qoIvd8x2v^HAl_KPAMof7YgrrCTBB*J`CPC1UU z0gV1=eO?pltKb$OEdO+52W&!1)xP zmKK4TkUFWyTJdt#p#a^G45K>2iSV8 zj{3^WlmaEO<;b>U5!ezmjx7+y6hlXTH<)8Y(fPfD9*s4JZnjuY^$lM4JNzO0MO-(b z0lqXb8>HWHqkc6tWYZZUg|vS7WoANuSJBWL9U!Y=uam^A{#_nbuHMQjGHPj<3Z?MI z`cMn-Eutke6DB??Aq}a5Ntlhm>tsRIIa zA5ySAV3qSsl6!wvDstD6zDmR+?#JCXHS?)s%W+H{bbqHTE(W7bGdenAc+=Utv93Py zi<@g{8p%PrNj*Z7yvoodfSMQxtKC~dJF$ASUQugMCuFYi{g9eFVV8fWFjpVonM?`{++jwa=q-LV~-U-p{6|S#Ns|9u@8~(qh3hWbGNNXHI<8hC5Izldgn@ zURAL-S?~7|?^UfiptS<(oTsh2>U>KcTR>q8*mahMV7Db~0VTSK2iR9LYXJma*b;mY zw-G4|EVim+LK%msXTEfh8AwnR2z^LTF;wZ-x*d3zle8R!~bG_ni-qS zLzumr#Vw*Y!OM?tRx8;U2r_Y&jZP~*`(^levADZk3N5X#{770=9WVD4WamIqi%vOO zrORy~%ESUP@zs9Y8o7M|wgL}Qu~cRYl}5no;XLe7TrIK_F7Cq6e>o#s7lb~`z zEpg6d=(bgQ^r^{pyV6B2zP1>8t5DANL);& zEgvb&@-%;Q^A6Jw+d~k>`4z-?Xg9^R(^bf_6Sh}N76vDKBw0BP)mXBO?@2|QIRRyj zj}eZ1z>_qNu`S^5?)7X>$0`v=$1tkMYSf$#9Ms=bdDKJw4>#8-el^{mZ+@yex#2o2s&hU>vQG#mi_E8JAjDB^=PWPy79~u|`4p3NC%+-p0zI zS1%RkpvOM3)R`XG*ZBu&3USdGC{5FHE${Wwe5% zm6k(~7k;QGU81xMhsuTf?^Vh;pmJ0MmAnrT{TT0c{^|V)Vjp~|hb0z8lI|o#BWn+j zGptFsL_Hf%SWVcx)HtTqFO z{}Jx?wICKM-zviLx3_}Fm}(8P9GlJK&*sTQx0)B=atI1WOBZhn<8)`J&hQvEH^Y_b z=swOeeHRoWsDSFAyWOQXIA}?cKd$UO7iN>jZQn1DEdXd|zRyGk;M#;?b5L3yyZJNp zhwvtV{r=PQQV6nG7)E(BAEPgwgwT_;i<#_!-07H8wTnZ)7=1Y7X@Ua;LlR$7?mM!x<{L zms>zmE~KkKjO98O68Hhq?t3)H-c@HOnrHd-H<4qJYH6H{vYlP`(2h=5*JVL$BpkQox6?rM}2X!SI&3FE&e+%*zG#Bi483^raWwEI+s$;$u2HMiZwoJ7i&z*h4I9 zs&CwVrfF6oI@4u%S-BUrcY1WqrRYqCZToH+b;HfmxHj++n(Jck*aDuHf$pBT7IAoh zLRfa9%FzQOyV&~}rP{KeP6>@RBuF$OPHEhg7I~Sv5J7tpafWu6b~dMNHH9G9YKk^D z38m&B z5I45t$#)Ds*G&eF=kogo>R$GFCThc_f&K2}z5|z60|pTF8~;G9lHWm@xEpDoQcZVa zglMrc2_qw79eU83hTmcpJe1hAYVo+{DZCu zzN|UZM|$AbCh%HvpOLpvX7cRV3U564&HS0_f`p~%w@i{p*2PM`8Lv++ubNgAi8gPo zKbpxAei>!RLxl#5W9=kOZUIt^T(-86)zEBsURhJ##giR7rrs`3n9b#~Ps3)5pieQ- z8jt!148lu9j%-dui=F;t2fav^+)g{)OTVsqvdA(-=-qy<^RDh{7O)LNW}xjyZ1)x* z=|0wBl(Yrhw8Sr(fuj9AeEQY#(=6Eh@4(rgp^o)b2~_n!Qa$Gw&VjLbo1xj;ZJR|; 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zy_Vo-A#j%6SvjaBQ=&zfQ9uJUh{6g3TH%|@)9Bf31JIV@7zSFWrVChGe$93Ns~dA!
zIuE@>bVblEKe9b*raF$SP$l@8Sd$)et$>>SS{t0;T7X`+hj`)$TbQvhN|+^v%0sjLl(^;
zi{_9;^M478#uwY@Hpw?jyDDAi5&$K^F?t0!r|o4gj|2;=i6;@bdqR0JRJ*aV%!nVy
zce`ivR+7NL_r8gSYz&HzkU)C8ZyMxUbe}6F1$O`XWb config/pipeline_config.yaml
+    ```
+
+### Trainer
+
+| Config Name | Type | Description |
+| --- | --- | --- |
+| `trainer.max_epochs` | integer | Maximum number of training epochs. |
+| `trainer.limit_train_batches` | integer | Limits the number of training batches per epoch. |
+| `trainer.check_val_every_n_epoch` | integer | Perform validation every n training epochs. |
+| `trainer.default_root_dir` | integer | Default path for logs and weights. |
+| `trainer.accumulate_grad_batches` | integer | Number of batches to accumulate gradients before updating. |
+
+### Model
+
+| Config Name | Type | Description |
+| --- | --- | --- |
+| `model.forecaster.class_path` | string | Forecaster module path (e.g., `probts.model.forecaster.point_forecaster.PatchTST`). |
+| `model.forecaster.init_args.{ARG}` | - | Model-specific hyperparameters. |
+| `model.num_samples` | integer | Number of samples per distribution during evaluation. |
+| `model.learning_rate` | float | Learning rate. |
+| `model.quantiles_num` | integer | Number of quantiles for evaluation. |
+
+
+### Data
+
+| Config Name | Type | Description |
+| --- | --- | --- |
+| `data.data_manager.init_args.dataset` | string | Dataset for training and evaluation. |
+| `data.data_manager.init_args.path` | string | Path to the dataset folder. |
+| `data.data_manager.init_args.split_val` | boolean | Whether to split a validation set during training. |
+| `data.data_manager.init_args.scaler` | string | Scaler type: `identity`, `standard` (z-score normalization), or `temporal` (scale based on average temporal absolute value). |
+| `data.data_manager.init_args.context_length` | integer | Length of observation window (required for long-term forecasting). |
+| `data.data_manager.init_args.prediction_length` | integer | Forecasting horizon length (required for long-term forecasting). |
+| `data.data_manager.init_args.target_dim` | integer | The number of variates. |
+| `data.data_manager.init_args.var_specific_norm` | boolean | If conduct per-variate normalization or not. |
+| `data.data_manager.init_args.timeenc` | integer | Time feature type. Select from `[0,1,2]`. See the explaination below for details. |
+| `data.batch_size` | integer | Batch size. |
+
+#### Temporal Features
+
+For the datasets used for long-term forecasting scenario, we support three types of time feature encoding
+
+```bash
+--data.data_manager.init_args.timeenc {the encoding type} # select from [0,1,2]
+```
+
+- **[timeenc 0] temporal information**
+
+    The dimension of time feature is 5, containing `month, day, weekday, hour, minute`.
+
+- **[timeenc 1] time feature based on frequency**
+    Extract time feature using `time_features_from_frequency_str()` function. The dimensionality follows:
+    ```bash
+    freq_map = {'h': 4, 't': 5, 's': 6, 'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3}
+    ```
+
+    Note: timeenc = 0 if model.embed != 'timeF' else 1.
+
+- **[timeenc 2] Raw date information**
+
+    The dimension of time feature is 5, using the following code to recover it to date data type:
+    ```bash
+    data_stamp = batch_data.past_time_feat.cpu().numpy().astype('datetime64[s]')
+    data_stamp = batch_data.future_time_feat.cpu().numpy().astype('datetime64[s]')
+    ```
+
+
+## Customized Model
+
+With our platform, you can easily evaluate customized models across various datasets. Follow the steps below to create and evaluate your model.
+
+
+### Step 1: Create a New Python File
+
+Create a new Python file and follow the structure below to define your custom model:
+
+```python
+from probts.model.forecaster import Forecaster
+
+class ModelName(Forecaster):
+    def __init__(
+        self,
+        **kwargs
+    ):
+        """
+        Initialize the model with parameters.
+        """
+        super().__init__(**kwargs)
+        # Initialize model parameters here
+
+    def forward(self, inputs):
+        """
+        Forward pass for the model.
+
+        Parameters:
+        inputs [Tensor]: Input tensor for the model.
+
+        Returns:
+        Tensor: Output tensor.
+        """
+        # Perform the forward pass of the model
+        return outputs
+
+    def loss(self, batch_data):
+        """
+        Compute the loss for the given batch data.
+
+        Parameters:
+        batch_data [dict]: Dictionary containing input data and possibly target data.
+
+        Returns:
+        Tensor: Computed loss.
+        """
+        # Extract inputs and targets from batch_data
+        inputs = batch_data.past_target_cdf[:, -self.context_length:, :] # [batch_size, context_length, var_num]
+        target = batch_data.future_target_cdf # [batch_size, prediction_length, var_num]
+
+        # Forward pass
+        outputs = self.forward(inputs)
+        
+        # Calculate loss using a loss function, e.g., Mean Squared Error
+        loss = self.loss_function(outputs, future_target_cdf)
+
+        return loss
+
+    def forecast(self, batch_data, num_samples=None):
+        """
+        Generate forecasts for the given batch data.
+
+        Parameters:
+        batch_data [dict]: Dictionary containing input data.
+        num_samples [int, optional]: Number of samples per distribution during evaluation. Defaults to None.
+
+        Returns:
+        Tensor: Forecasted outputs.
+        """
+        # Perform the forward pass to get the outputs
+        outputs = self(batch_data.past_target_cdf[:, -self.context_length:, :])
+
+        if num_samples is not None:
+            # If num_samples is specified, use it to sample from the distribution
+            outputs = self.sample_from_distribution(outputs, num_samples)
+        else: 
+            # If perform point estimation, the num_samples is equal to 1
+            outputs = outputs.unsqueeze(1)
+        return outputs # [batch_size, num_samples, prediction_length, var_num]
+```
+
+#### Input Data Format
+
+The `batch_data` dictionary contains several fields that provide necessary information for the model's operation. Each field is described below:
+
+- **`target_dimension_indicator`**: 
+  - **Shape**: [var_num]
+  - **Description**: Indicator that specifies which dimension or feature of the target is being referenced. 
+
+- **`{past|future}_time_feat`**: 
+  - **Shape**: [batch_size,length,time_feature_dim]
+  - **Description**: Time features associated with each time step in the past or future. This can include various time-related information such as timestamps, seasonal indicators (e.g., month, day of the week), or other temporal features that provide context to the observations.
+- **`{past|future}_target_cdf`**: 
+  - **Shape**: [batch_size,length,var_num]
+  - **Description**: The observation values of the target variable(s) for past or future time steps. 
+- **`{past|future}_observed_values`**: 
+  - **Shape**: [batch_size,length,var_num]
+  - **Description**: Binary masks indicating which values in the past or future target data are observed (1) and which are missing or unobserved (0). 
+
+### Step 2: Create YAML Configuration File
+
+Create a YAML configuration file (`model.yaml`) for the customized model:
+
+```yaml
+seed_everything: 1 # random seed
+trainer:
+  accelerator: gpu
+  devices: 1
+  strategy: auto
+  max_epochs: 50
+  use_distributed_sampler: false
+  limit_train_batches: 100
+  log_every_n_steps: 1
+  default_root_dir: ./results # path to the log folder
+model:
+  forecaster:
+    class_path: class.path.to.ModelName
+    init_args:
+      # init your hyperparameter here
+  learning_rate: 0.001 # learning rate
+data:
+  data_manager:
+    class_path: probts.data.data_manager.DataManager
+    init_args:
+      dataset: solar_nips # dataset name
+      split_val: true
+      scaler: standard # identity, standard, temporal
+  batch_size: 32
+  test_batch_size: 32
+  num_workers: 8
+```
+
+### Step 3: Run the Customized Model
+
+Run the customized model using the configuration file:
+
+```bash
+python run.py --config config/path/to/model.yaml
+```
\ No newline at end of file
diff --git a/docs/figs/probts_framework.png b/docs/figs/probts_framework.png
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diff --git a/probts/__init__.py b/probts/__init__.py
new file mode 100644
index 0000000..f881c9c
--- /dev/null
+++ b/probts/__init__.py
@@ -0,0 +1,3 @@
+from .data import *
+from .model import *
+from .utils import *
\ No newline at end of file
diff --git a/probts/callbacks/__init__.py b/probts/callbacks/__init__.py
new file mode 100644
index 0000000..e1739a7
--- /dev/null
+++ b/probts/callbacks/__init__.py
@@ -0,0 +1,2 @@
+from .memory_callback import MemoryCallback
+from .time_callback import TimeCallback
\ No newline at end of file
diff --git a/probts/callbacks/memory_callback.py b/probts/callbacks/memory_callback.py
new file mode 100644
index 0000000..c036a50
--- /dev/null
+++ b/probts/callbacks/memory_callback.py
@@ -0,0 +1,129 @@
+import gc
+import threading
+import psutil
+import torch
+
+import lightning.pytorch as pl
+from lightning.pytorch.callbacks.callback import Callback
+
+
+def byte2gb(x):
+    return float(x / 2**30)
+
+
+class MemoryTrace:
+    def __init__(self):
+        gc.collect()
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
+        self.begin = byte2gb(torch.cuda.memory_allocated())
+        self.process = psutil.Process()
+        self.cpu_begin = byte2gb(self.cpu_mem_used())
+        self.peak_monitoring = True
+        peak_monitor_thread = threading.Thread(target=self.peak_monitor_func)
+        peak_monitor_thread.daemon = True
+        peak_monitor_thread.start()
+
+    def cpu_mem_used(self):
+        """get resident set size memory for the current process"""
+        return self.process.memory_info().rss
+
+    def peak_monitor_func(self):
+        self.cpu_peak = -1
+
+        while True:
+            self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak)
+
+            if not self.peak_monitoring:
+                break
+
+    def __exit__(self, *exc):
+        self.peak_monitoring = False
+
+        gc.collect()
+        torch.cuda.empty_cache()
+        self.end = byte2gb(torch.cuda.memory_allocated())
+        self.peak = byte2gb(torch.cuda.max_memory_allocated())
+        cuda_info = torch.cuda.memory_stats()
+        self.peak_active_gb = byte2gb(cuda_info["active_bytes.all.peak"])
+        self.cuda_malloc_retires = cuda_info.get("num_alloc_retries", 0)
+        self.m_cuda_ooms = cuda_info.get("num_ooms", 0)
+        self.used = byte2gb(self.end - self.begin)
+        self.peaked = byte2gb(self.peak - self.begin)
+        self.max_reserved = byte2gb(torch.cuda.max_memory_reserved())
+
+        self.cpu_end = self.cpu_mem_used()
+        self.cpu_used = byte2gb(self.cpu_end - self.cpu_begin)
+        self.cpu_peaked = byte2gb(self.cpu_peak - self.cpu_begin)
+
+
+class MemoryCallback(Callback):
+    """
+        Trace the memory usage.
+    """
+    def __init__(self):
+        self.memory_summary = {
+            'train': {},
+            'val': {},
+            'test': {}
+        }
+    
+    def update_memory_summary(self, key, memtrace):
+        self.memory_summary[key] = {
+            "mem_peak": max(memtrace.peak, self.memory_summary[key].get("mem_peak", 0)),
+            "max_reserved": max(memtrace.max_reserved, self.memory_summary[key].get("max_reserved", 0)),
+            "peak_active_gb": max(memtrace.peak_active_gb, self.memory_summary[key].get("peak_active_gb", 0)),
+            "cuda_malloc_retires": max(memtrace.cuda_malloc_retires, self.memory_summary[key].get("cuda_malloc_retires", 0)),
+            "cpu_total_peaked": max(memtrace.cpu_peaked + memtrace.cpu_begin, self.memory_summary[key].get("cpu_total_peaked", 0)),
+        }
+    
+    def on_train_epoch_start(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule"
+    ) -> None:
+        """Called when the train epoch begins"""
+        self.train_memtrace = MemoryTrace()
+    
+    def on_train_epoch_end(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule"
+    ) -> None:
+        """Called when the train epoch ends"""
+        self.train_memtrace.__exit__()
+        self.update_memory_summary('train', self.train_memtrace)
+
+    def on_validation_epoch_start(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule"
+    ) -> None:
+        """Called when the validation epoch begins"""
+        self.val_memtrace = MemoryTrace()
+    
+    def on_validation_epoch_end(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule"
+    ) -> None:
+        """Called when the validation epoch ends"""
+        self.val_memtrace.__exit__()
+        self.update_memory_summary('val', self.val_memtrace)
+    
+    def on_test_epoch_start(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule"
+    ) -> None:
+        """Called when the test epoch begins"""
+        self.test_memtrace = MemoryTrace()
+    
+    def on_test_epoch_end(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule"
+    ) -> None:
+        """Called when the test epoch ends"""
+        self.test_memtrace.__exit__()
+        self.update_memory_summary('test', self.test_memtrace)
diff --git a/probts/callbacks/time_callback.py b/probts/callbacks/time_callback.py
new file mode 100644
index 0000000..a5b3f1a
--- /dev/null
+++ b/probts/callbacks/time_callback.py
@@ -0,0 +1,76 @@
+import time
+from typing import Any
+
+import lightning.pytorch as pl
+from lightning.pytorch.utilities.types import STEP_OUTPUT
+from lightning.pytorch.callbacks.callback import Callback
+
+
+class TimeCallback(Callback):
+    """
+        Trace the computation time.
+    """
+    def __init__(self):
+        self.time_summary = {
+            'train_batch_time': [],
+            'val_batch_time': [],
+            'test_batch_time': []
+        }
+    
+    def on_train_batch_start(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int
+    ) -> None:
+        """Called when the train batch begins."""
+        self.train_start_time = time.time()
+    
+    def on_train_batch_end(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any, batch_idx: int
+    ) -> None:
+        """Called when the train batch ends"""
+        self.time_summary['train_batch_time'].append(time.time() - self.train_start_time)
+
+    def on_validation_batch_start(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule",
+        batch: Any,
+        batch_idx: int,
+        dataloader_idx: int = 0,
+    ) -> None:
+        """Called when the validation batch begins"""
+        self.val_start_time = time.time()
+    
+    def on_validation_batch_end(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule",
+        outputs: STEP_OUTPUT,
+        batch: Any,
+        batch_idx: int,
+        dataloader_idx: int = 0,
+    ) -> None:
+        """Called when the validation batch ends"""
+        self.time_summary['val_batch_time'].append(time.time() - self.val_start_time)
+    
+    def on_test_batch_start(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule",
+        batch: Any,
+        batch_idx: int,
+        dataloader_idx: int = 0,
+    ) -> None:
+        """Called when the test batch begins"""
+        self.test_start_time = time.time()
+    
+    def on_test_batch_end(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule",
+        outputs: STEP_OUTPUT,
+        batch: Any,
+        batch_idx: int,
+        dataloader_idx: int = 0,
+    ) -> None:
+        """Called when the test batch ends"""
+        self.time_summary['test_batch_time'].append(time.time() - self.test_start_time)
diff --git a/probts/data/__init__.py b/probts/data/__init__.py
new file mode 100644
index 0000000..2ad2a55
--- /dev/null
+++ b/probts/data/__init__.py
@@ -0,0 +1,3 @@
+from .data_module import *
+from .data_manager import *
+from .time_features import *
\ No newline at end of file
diff --git a/probts/data/data_loader.py b/probts/data/data_loader.py
new file mode 100644
index 0000000..e03a045
--- /dev/null
+++ b/probts/data/data_loader.py
@@ -0,0 +1,403 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from Autoformer
+# - Source: https://github.com/thuml/Autoformer/tree/main
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+import os
+import numpy as np
+import pandas as pd
+import os
+import torch
+from torch.utils.data import Dataset
+from sklearn.preprocessing import StandardScaler
+from probts.utils.timefeatures import time_features
+import warnings
+
+warnings.filterwarnings('ignore')
+
+
+class Dataset_ETT_hour(Dataset):
+    def __init__(self, root_path, flag='train', size=None,
+                 features='S', data_path='ETTh1.csv',
+                 target='OT', scale=True, timeenc=0, freq='h'):
+        # size [seq_len, label_len, pred_len]
+        # info
+        if size == None:
+            self.seq_len = 24 * 4 * 4
+            self.label_len = 24 * 4
+            self.pred_len = 24 * 4
+        else:
+            self.seq_len = size[0]
+            self.label_len = size[1]
+            self.pred_len = size[2]
+        # init
+        assert flag in ['train', 'test', 'val']
+        type_map = {'train': 0, 'val': 1, 'test': 2}
+        self.set_type = type_map[flag]
+
+        self.features = features
+        self.target = target
+        self.scale = scale
+        self.timeenc = timeenc
+        self.freq = freq
+
+        self.root_path = root_path
+        self.data_path = data_path
+        self.__read_data__()
+
+    def __read_data__(self):
+        self.scaler = StandardScaler()
+        df_raw = pd.read_csv(os.path.join(self.root_path,
+                                          self.data_path))
+
+        border1s = [0, 12 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len]
+        border2s = [12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24]
+        border1 = border1s[self.set_type]
+        border2 = border2s[self.set_type]
+
+        if self.features == 'M' or self.features == 'MS':
+            cols_data = df_raw.columns[1:]
+            df_data = df_raw[cols_data]
+        elif self.features == 'S':
+            df_data = df_raw[[self.target]]
+
+        if self.scale:
+            train_data = df_data[border1s[0]:border2s[0]]
+            self.scaler.fit(train_data.values)
+            data = self.scaler.transform(df_data.values)
+        else:
+            data = df_data.values
+
+        df_stamp = df_raw[['date']][border1:border2]
+        df_stamp['date'] = pd.to_datetime(df_stamp.date)
+        if self.timeenc == 0:
+            df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
+            df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
+            df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
+            df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
+            data_stamp = df_stamp.drop(['date'], axis=1).values
+        elif self.timeenc == 1:
+            data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
+            data_stamp = data_stamp.transpose(1, 0)
+
+        self.data_x = data[border1:border2]
+        self.data_y = data[border1:border2]
+        self.data_stamp = data_stamp
+
+    def __getitem__(self, index):
+        s_begin = index
+        s_end = s_begin + self.seq_len
+        r_begin = s_end - self.label_len
+        r_end = r_begin + self.label_len + self.pred_len
+
+        seq_x = self.data_x[s_begin:s_end]
+        seq_y = self.data_y[r_begin:r_end]
+        seq_x_mark = self.data_stamp[s_begin:s_end]
+        seq_y_mark = self.data_stamp[r_begin:r_end]
+
+        return seq_x, seq_y, seq_x_mark, seq_y_mark
+
+    def __len__(self):
+        return len(self.data_x) - self.seq_len - self.pred_len + 1
+
+    def inverse_transform(self, data):
+        return self.scaler.inverse_transform(data)
+
+
+class Dataset_ETT_minute(Dataset):
+    def __init__(self, root_path, flag='train', size=None,
+                 features='S', data_path='ETTm1.csv',
+                 target='OT', scale=True, timeenc=0, freq='t'):
+        # size [seq_len, label_len, pred_len]
+        # info
+        if size == None:
+            self.seq_len = 24 * 4 * 4
+            self.label_len = 24 * 4
+            self.pred_len = 24 * 4
+        else:
+            self.seq_len = size[0]
+            self.label_len = size[1]
+            self.pred_len = size[2]
+        # init
+        assert flag in ['train', 'test', 'val']
+        type_map = {'train': 0, 'val': 1, 'test': 2}
+        self.set_type = type_map[flag]
+
+        self.features = features
+        self.target = target
+        self.scale = scale
+        self.timeenc = timeenc
+        self.freq = freq
+
+        self.root_path = root_path
+        self.data_path = data_path
+        self.__read_data__()
+
+    def __read_data__(self):
+        self.scaler = StandardScaler()
+        df_raw = pd.read_csv(os.path.join(self.root_path,
+                                          self.data_path))
+
+        border1s = [0, 12 * 30 * 24 * 4 - self.seq_len, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4 - self.seq_len]
+        border2s = [12 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4]
+        border1 = border1s[self.set_type]
+        border2 = border2s[self.set_type]
+
+        if self.features == 'M' or self.features == 'MS':
+            cols_data = df_raw.columns[1:]
+            df_data = df_raw[cols_data]
+        elif self.features == 'S':
+            df_data = df_raw[[self.target]]
+
+        if self.scale:
+            train_data = df_data[border1s[0]:border2s[0]]
+            self.scaler.fit(train_data.values)
+            data = self.scaler.transform(df_data.values)
+        else:
+            data = df_data.values
+
+        df_stamp = df_raw[['date']][border1:border2]
+        df_stamp['date'] = pd.to_datetime(df_stamp.date)
+        if self.timeenc == 0:
+            df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
+            df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
+            df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
+            df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
+            df_stamp['minute'] = df_stamp.date.apply(lambda row: row.minute, 1)
+            df_stamp['minute'] = df_stamp.minute.map(lambda x: x // 15)
+            data_stamp = df_stamp.drop(['date'], axis=1).values
+        elif self.timeenc == 1:
+            data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
+            data_stamp = data_stamp.transpose(1, 0)
+
+        self.data_x = data[border1:border2]
+        self.data_y = data[border1:border2]
+        self.data_stamp = data_stamp
+
+    def __getitem__(self, index):
+        s_begin = index
+        s_end = s_begin + self.seq_len
+        r_begin = s_end - self.label_len
+        r_end = r_begin + self.label_len + self.pred_len
+
+        seq_x = self.data_x[s_begin:s_end]
+        seq_y = self.data_y[r_begin:r_end]
+        seq_x_mark = self.data_stamp[s_begin:s_end]
+        seq_y_mark = self.data_stamp[r_begin:r_end]
+
+        return seq_x, seq_y, seq_x_mark, seq_y_mark
+
+    def __len__(self):
+        return len(self.data_x) - self.seq_len - self.pred_len + 1
+
+    def inverse_transform(self, data):
+        return self.scaler.inverse_transform(data)
+
+
+class Dataset_Custom(Dataset):
+    def __init__(self, root_path, flag='train', size=None,
+                 features='S', data_path='ETTh1.csv',
+                 target='OT', scale=True, timeenc=0, freq='h'):
+        # size [seq_len, label_len, pred_len]
+        # info
+        if size == None:
+            self.seq_len = 24 * 4 * 4
+            self.label_len = 24 * 4
+            self.pred_len = 24 * 4
+        else:
+            self.seq_len = size[0]
+            self.label_len = size[1]
+            self.pred_len = size[2]
+        # init
+        assert flag in ['train', 'test', 'val']
+        type_map = {'train': 0, 'val': 1, 'test': 2}
+        self.set_type = type_map[flag]
+
+        self.features = features
+        self.target = target
+        self.scale = scale
+        self.timeenc = timeenc
+        self.freq = freq
+
+        self.root_path = root_path
+        self.data_path = data_path
+        self.__read_data__()
+
+    def __read_data__(self):
+        self.scaler = StandardScaler()
+        df_raw = pd.read_csv(os.path.join(self.root_path,
+                                          self.data_path))
+
+        '''
+        df_raw.columns: ['date', ...(other features), target feature]
+        '''
+        cols = list(df_raw.columns)
+        cols.remove(self.target)
+        cols.remove('date')
+        df_raw = df_raw[['date'] + cols + [self.target]]
+        # print(cols)
+        num_train = int(len(df_raw) * 0.7)
+        num_test = int(len(df_raw) * 0.2)
+        num_vali = len(df_raw) - num_train - num_test
+        border1s = [0, num_train - self.seq_len, len(df_raw) - num_test - self.seq_len]
+        border2s = [num_train, num_train + num_vali, len(df_raw)]
+        border1 = border1s[self.set_type]
+        border2 = border2s[self.set_type]
+
+        if self.features == 'M' or self.features == 'MS':
+            cols_data = df_raw.columns[1:]
+            df_data = df_raw[cols_data]
+        elif self.features == 'S':
+            df_data = df_raw[[self.target]]
+
+        if self.scale:
+            train_data = df_data[border1s[0]:border2s[0]]
+            self.scaler.fit(train_data.values)
+            # print(self.scaler.mean_)
+            # exit()
+            data = self.scaler.transform(df_data.values)
+        else:
+            data = df_data.values
+
+        df_stamp = df_raw[['date']][border1:border2]
+        df_stamp['date'] = pd.to_datetime(df_stamp.date)
+        if self.timeenc == 0:
+            df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
+            df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
+            df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
+            df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
+            data_stamp = df_stamp.drop(['date'], axis=1).values
+        elif self.timeenc == 1:
+            data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
+            data_stamp = data_stamp.transpose(1, 0)
+
+        self.data_x = data[border1:border2]
+        self.data_y = data[border1:border2]
+        self.data_stamp = data_stamp
+
+    def __getitem__(self, index):
+        s_begin = index
+        s_end = s_begin + self.seq_len
+        r_begin = s_end - self.label_len
+        r_end = r_begin + self.label_len + self.pred_len
+
+        seq_x = self.data_x[s_begin:s_end]
+        seq_y = self.data_y[r_begin:r_end]
+        seq_x_mark = self.data_stamp[s_begin:s_end]
+        seq_y_mark = self.data_stamp[r_begin:r_end]
+
+        return seq_x, seq_y, seq_x_mark, seq_y_mark
+
+    def __len__(self):
+        return len(self.data_x) - self.seq_len - self.pred_len + 1
+
+    def inverse_transform(self, data):
+        return self.scaler.inverse_transform(data)
+    
+
+class Dataset_Pred(Dataset):
+    def __init__(self, root_path, flag='pred', size=None,
+                 features='S', data_path='ETTh1.csv',
+                 target='OT', scale=True, inverse=False, timeenc=0, freq='15min', cols=None):
+        # size [seq_len, label_len, pred_len]
+        # info
+        if size == None:
+            self.seq_len = 24 * 4 * 4
+            self.label_len = 24 * 4
+            self.pred_len = 24 * 4
+        else:
+            self.seq_len = size[0]
+            self.label_len = size[1]
+            self.pred_len = size[2]
+        # init
+        assert flag in ['pred']
+
+        self.features = features
+        self.target = target
+        self.scale = scale
+        self.inverse = inverse
+        self.timeenc = timeenc
+        self.freq = freq
+        self.cols = cols
+        self.root_path = root_path
+        self.data_path = data_path
+        self.__read_data__()
+
+    def __read_data__(self):
+        self.scaler = StandardScaler()
+        df_raw = pd.read_csv(os.path.join(self.root_path,
+                                          self.data_path))
+        '''
+        df_raw.columns: ['date', ...(other features), target feature]
+        '''
+        if self.cols:
+            cols = self.cols.copy()
+            cols.remove(self.target)
+        else:
+            cols = list(df_raw.columns)
+            cols.remove(self.target)
+            cols.remove('date')
+        df_raw = df_raw[['date'] + cols + [self.target]]
+        border1 = len(df_raw) - self.seq_len
+        border2 = len(df_raw)
+
+        if self.features == 'M' or self.features == 'MS':
+            cols_data = df_raw.columns[1:]
+            df_data = df_raw[cols_data]
+        elif self.features == 'S':
+            df_data = df_raw[[self.target]]
+
+        if self.scale:
+            self.scaler.fit(df_data.values)
+            data = self.scaler.transform(df_data.values)
+        else:
+            data = df_data.values
+
+        tmp_stamp = df_raw[['date']][border1:border2]
+        tmp_stamp['date'] = pd.to_datetime(tmp_stamp.date)
+        pred_dates = pd.date_range(tmp_stamp.date.values[-1], periods=self.pred_len + 1, freq=self.freq)
+
+        df_stamp = pd.DataFrame(columns=['date'])
+        df_stamp.date = list(tmp_stamp.date.values) + list(pred_dates[1:])
+        if self.timeenc == 0:
+            df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
+            df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
+            df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
+            df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
+            df_stamp['minute'] = df_stamp.date.apply(lambda row: row.minute, 1)
+            df_stamp['minute'] = df_stamp.minute.map(lambda x: x // 15)
+            data_stamp = df_stamp.drop(['date'], axis=1).values
+        elif self.timeenc == 1:
+            data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
+            data_stamp = data_stamp.transpose(1, 0)
+
+        self.data_x = data[border1:border2]
+        if self.inverse:
+            self.data_y = df_data.values[border1:border2]
+        else:
+            self.data_y = data[border1:border2]
+        self.data_stamp = data_stamp
+
+    def __getitem__(self, index):
+        s_begin = index
+        s_end = s_begin + self.seq_len
+        r_begin = s_end - self.label_len
+        r_end = r_begin + self.label_len + self.pred_len
+
+        seq_x = self.data_x[s_begin:s_end]
+        if self.inverse:
+            seq_y = self.data_x[r_begin:r_begin + self.label_len]
+        else:
+            seq_y = self.data_y[r_begin:r_begin + self.label_len]
+        seq_x_mark = self.data_stamp[s_begin:s_end]
+        seq_y_mark = self.data_stamp[r_begin:r_end]
+
+        return seq_x, seq_y, seq_x_mark, seq_y_mark
+
+    def __len__(self):
+        return len(self.data_x) - self.seq_len + 1
+
+    def inverse_transform(self, data):
+        return self.scaler.inverse_transform(data)
diff --git a/probts/data/data_manager.py b/probts/data/data_manager.py
new file mode 100644
index 0000000..7f6a206
--- /dev/null
+++ b/probts/data/data_manager.py
@@ -0,0 +1,308 @@
+import torch
+from copy import deepcopy
+from pathlib import Path
+
+from gluonts.dataset.common import ListDataset
+from gluonts.dataset.field_names import FieldName
+from gluonts.dataset.repository import dataset_names, datasets
+from gluonts.dataset.multivariate_grouper import MultivariateGrouper
+
+from .ltsf_datasets import get_LTSF_info, get_LTSF_borders, get_LTSF_Dataset
+from .stsf_datasets import GluonTSDataset
+from .time_features import get_lags
+
+from probts.utils import StandardScaler, TemporalScaler, IdentityScaler
+
+
+MULTI_VARIATE_DATASETS = [
+    'exchange_rate_nips',
+    'solar_nips',
+    'electricity_nips',
+    'traffic_nips',
+    'taxi_30min',
+    'wiki-rolling_nips',
+    'wiki2000_nips'
+]
+
+
+class ProbTSBatchData:
+    input_names_ = [
+        'target_dimension_indicator',
+        'past_time_feat',
+        'past_target_cdf',
+        'past_observed_values',
+        'past_is_pad',
+        'future_time_feat',
+        'future_target_cdf',
+        'future_observed_values',
+    ]
+    
+    def __init__(self, data_dict, device):
+        self.__dict__.update(data_dict)
+        if len(self.__dict__['past_target_cdf'].shape) == 2:
+            self.expand_dim()
+        self.set_device(device)
+        self.fill_inputs()
+        self.process_pad()
+
+    def fill_inputs(self):
+        for input in self.input_names_:
+            if input not in self.__dict__:
+                self.__dict__[input] = None
+
+    def set_device(self, device):
+        for k, v in self.__dict__.items():
+            if v is not None:
+                v.to(device)
+        self.device = device
+
+    def expand_dim(self):
+        self.__dict__["target_dimension_indicator"] = self.__dict__["target_dimension_indicator"][:, :1]
+        for input in ['past_target_cdf','past_observed_values','future_target_cdf','future_observed_values']:
+            self.__dict__[input] = self.__dict__[input].unsqueeze(-1)
+
+    def process_pad(self):
+        if self.__dict__['past_is_pad'] is not None:
+            self.__dict__["past_observed_values"] = torch.min(
+                self.__dict__["past_observed_values"],
+                1 - self.__dict__["past_is_pad"].unsqueeze(-1)
+            )
+
+
+class DataManager:
+
+    def __init__(
+        self,
+        dataset,
+        path: str = './datasets',
+        history_length: int = None,
+        context_length: int = None,
+        prediction_length: int = None,
+        test_rolling_length: int = 96,
+        split_val: bool = True,
+        scaler: str = 'none',
+        test_sampling: str = 'ctx', # ['arrange', 'ctx', 'pred']
+        context_length_factor: int = 1,
+        timeenc: int = 1,
+        var_specific_norm: bool = True,
+    ):
+        self.dataset = dataset
+        self.test_rolling_length = test_rolling_length
+        self.global_mean = None
+        self.multivariate = True 
+        self.split_val = split_val
+        self.test_sampling = test_sampling
+        self.timeenc = timeenc
+        self.var_specific_norm = var_specific_norm
+
+        if scaler == 'standard':
+            self.scaler = StandardScaler(var_specific=self.var_specific_norm)
+        elif scaler == 'temporal':
+            self.scaler = TemporalScaler()
+        else:
+            self.scaler = IdentityScaler()
+    
+        if dataset in dataset_names:
+            """
+            Use gluonts to load short-term datasets.
+            """
+            print("Loading Short-term Datasets: {dataset}".format(dataset=dataset))
+            self.dataset_raw = datasets.get_dataset(dataset, path=Path(path), regenerate=False)
+
+            # Meta parameters
+            self.target_dim = int(self.dataset_raw.metadata.feat_static_cat[0].cardinality)
+            self.freq = self.dataset_raw.metadata.freq.upper()
+            self.lags_list = get_lags(self.freq)
+            self.prediction_length = self.dataset_raw.metadata.prediction_length \
+                if prediction_length is None else prediction_length
+            self.context_length = self.dataset_raw.metadata.prediction_length * context_length_factor \
+                if context_length is None else context_length
+            self.history_length = (self.context_length + max(self.lags_list)) \
+                if history_length is None else history_length
+
+            self.prepare_STSF_dataset(dataset)
+        else:
+            """
+            Load long-term datasets.
+            """
+            print("Loading Long-term Datasets: {dataset}".format(dataset=dataset))
+
+            if context_length is None or prediction_length is None:
+                raise ValueError("The context_length or prediction_length is not assigned.")
+
+            data_path, self.freq = get_LTSF_info(dataset)
+            self.dataset_raw, self.data_stamp, self.target_dim, data_size = get_LTSF_Dataset(path, data_path,freq=self.freq,timeenc=self.timeenc)
+            self.border_begin, self.border_end = get_LTSF_borders(dataset, data_size)
+
+            assert data_size >= self.border_end[2], print("\n The end index larger then data size!")
+
+            # Meta parameters
+            self.lags_list = get_lags(self.freq)
+            self.prediction_length = prediction_length
+            self.context_length = context_length
+            self.history_length = (self.context_length + max(self.lags_list)) \
+                if history_length is None else history_length
+
+            self.prepare_LTSF_dataset()
+
+        print(f"context_length: {self.context_length}, prediction_length: {self.prediction_length}")
+        if scaler == 'standard':
+            print(f"variate-specific normalization: {self.var_specific_norm}")
+
+    def prepare_LTSF_dataset(self):
+        train_data = self.dataset_raw[: self.border_end[0]]
+        val_data = self.dataset_raw[: self.border_end[1]]
+        test_data = self.dataset_raw[: self.border_end[2]]
+        
+        self.scaler.fit(torch.tensor(train_data.values))
+        
+        train_set = self.df_to_mvds(train_data, freq=self.freq)
+        val_set = self.df_to_mvds(val_data,freq=self.freq)
+        test_set = self.df_to_mvds(test_data,freq=self.freq)
+        
+        train_grouper = MultivariateGrouper(max_target_dim=self.target_dim)
+        test_grouper = MultivariateGrouper(max_target_dim=self.target_dim)
+        
+        group_train_set = train_grouper(train_set)
+        group_val_set = test_grouper(val_set)
+        group_test_set = test_grouper(test_set)
+        
+        group_val_set = self.get_rolling_test(group_val_set, self.border_begin[1], self.border_end[1], rolling_length=self.test_rolling_length)
+        group_test_set = self.get_rolling_test(group_test_set, self.border_begin[2], self.border_end[2], rolling_length=self.test_rolling_length)
+        
+        stsf_dataset_loader = GluonTSDataset(
+            ProbTSBatchData.input_names_, 
+            self.history_length,
+            self.prediction_length,
+            self.freq,
+            self.multivariate
+        )
+        
+        self.train_iter_dataset = stsf_dataset_loader.get_iter_dataset(group_train_set, mode='train', data_stamp=self.data_stamp[: self.border_end[0]])
+        self.val_iter_dataset = stsf_dataset_loader.get_iter_dataset(group_val_set, mode='val', data_stamp=self.data_stamp[: self.border_end[1]])
+        self.test_iter_dataset = stsf_dataset_loader.get_iter_dataset(group_test_set, mode='test', data_stamp=self.data_stamp[: self.border_end[2]])
+        self.time_feat_dim = stsf_dataset_loader.time_feat_dim
+        self.global_mean = torch.mean(torch.tensor(group_train_set[0]['target']), dim=-1)
+
+    def df_to_mvds(self, df, freq='H'):
+        datasets = []
+        for variable in df.keys():
+            ds = {"item_id" : variable, "target" : df[variable], "start": str(df.index[0])}
+            datasets.append(ds)
+        dataset = ListDataset(datasets,freq=freq)
+        return dataset
+
+    def get_rolling_test(self, test_set, border_begin_idx, border_end_idx, rolling_length):
+        num_test_dates = int(((border_end_idx - border_begin_idx - self.prediction_length) / rolling_length))
+        print("num_test_dates: ", num_test_dates)
+
+        test_set = next(iter(test_set))
+        rolling_test_seq_list = list()
+        for i in range(num_test_dates):
+            rolling_test_seq = deepcopy(test_set)
+            rolling_end = border_begin_idx + self.prediction_length + i * rolling_length
+            rolling_test_seq[FieldName.TARGET] = rolling_test_seq[FieldName.TARGET][:, :rolling_end]
+            rolling_test_seq_list.append(rolling_test_seq)
+
+        rolling_test_set = ListDataset(
+            rolling_test_seq_list, freq=self.freq, one_dim_target=False
+        )
+        return rolling_test_set
+
+    def split_train_val(self, train_set):
+        trunc_train_list = []
+        val_set_list = []
+        univariate = False
+
+        for train_seq in iter(train_set):
+            # truncate train set
+            offset = self.num_test_dates * self.prediction_length
+            trunc_train_seq = deepcopy(train_seq)
+
+            if len(train_seq[FieldName.TARGET].shape) == 1:
+                trunc_train_len = train_seq[FieldName.TARGET].shape[0] - offset
+                trunc_train_seq[FieldName.TARGET] = train_seq[FieldName.TARGET][:trunc_train_len]
+                univariate = True
+            elif len(train_seq[FieldName.TARGET].shape) == 2:
+                trunc_train_len = train_seq[FieldName.TARGET].shape[1] - offset
+                trunc_train_seq[FieldName.TARGET] = train_seq[FieldName.TARGET][:, :trunc_train_len]
+            else:
+                raise ValueError(f"Invalid Data Shape: {str(len(train_seq[FieldName.TARGET].shape))}")
+
+            trunc_train_list.append(trunc_train_seq)
+
+            # construct val set by rolling
+            for i in range(self.num_test_dates):
+                val_seq = deepcopy(train_seq)
+                rolling_len = trunc_train_len + self.prediction_length * (i+1)
+                if univariate:
+                    val_seq[FieldName.TARGET] = val_seq[FieldName.TARGET][trunc_train_len + self.prediction_length * (i-1) - self.context_length : rolling_len]
+                else:
+                    val_seq[FieldName.TARGET] = val_seq[FieldName.TARGET][:, :rolling_len]
+                
+                val_set_list.append(val_seq)
+
+        trunc_train_set = ListDataset(
+            trunc_train_list, freq=self.freq, one_dim_target=univariate
+        )
+
+        val_set = ListDataset(
+            val_set_list, freq=self.freq, one_dim_target=univariate
+        )
+        
+        return trunc_train_set, val_set
+
+    def truncate_test(self, test_set):
+        trunc_test_list = []
+        for test_seq in iter(test_set):
+            # truncate train set
+            trunc_test_seq = deepcopy(test_seq)
+
+            trunc_test_seq[FieldName.TARGET] = trunc_test_seq[FieldName.TARGET][- ( self.prediction_length * 2 + self.context_length):]
+
+            trunc_test_list.append(trunc_test_seq)
+
+        trunc_test_set = ListDataset(
+            trunc_test_list, freq=self.freq, one_dim_target=True
+        )
+
+        return trunc_test_set
+    
+    def prepare_STSF_dataset(self, dataset: str):
+        if dataset in MULTI_VARIATE_DATASETS:
+            self.num_test_dates = int(len(self.dataset_raw.test)/len(self.dataset_raw.train))
+
+            train_grouper = MultivariateGrouper(max_target_dim=int(self.target_dim))
+            test_grouper = MultivariateGrouper(
+                num_test_dates=self.num_test_dates, 
+                max_target_dim=int(self.target_dim)
+            )
+            train_set = train_grouper(self.dataset_raw.train)
+            test_set = test_grouper(self.dataset_raw.test)
+            self.scaler.fit(torch.tensor(train_set[0]['target'].transpose(1, 0)))
+            self.global_mean = torch.mean(torch.tensor(train_set[0]['target']), dim=-1)
+        else:
+            self.target_dim = 1
+            self.multivariate = False
+            self.num_test_dates = 1
+            train_set = self.dataset_raw.train
+            test_set = self.dataset_raw.test
+            test_set = self.truncate_test(test_set)
+
+        if self.split_val:
+            train_set, val_set = self.split_train_val(train_set)
+        else:
+            val_set = test_set
+
+        stsf_dataset_loader = GluonTSDataset(
+            ProbTSBatchData.input_names_, 
+            self.history_length,
+            self.prediction_length,
+            self.freq,
+            self.multivariate
+        )
+
+        self.train_iter_dataset = stsf_dataset_loader.get_iter_dataset(train_set, mode='train')
+        self.val_iter_dataset = stsf_dataset_loader.get_iter_dataset(val_set, mode='val')
+        self.test_iter_dataset = stsf_dataset_loader.get_iter_dataset(test_set, mode='test')
+        self.time_feat_dim = stsf_dataset_loader.time_feat_dim
diff --git a/probts/data/data_module.py b/probts/data/data_module.py
new file mode 100644
index 0000000..e073b38
--- /dev/null
+++ b/probts/data/data_module.py
@@ -0,0 +1,46 @@
+import os
+import lightning.pytorch as pl
+from torch.utils.data import DataLoader
+from .data_manager import DataManager
+
+class ProbTSDataModule(pl.LightningDataModule):
+    r"""
+        DataModule for probablistic time series datasets.
+    """
+    def __init__(
+        self,
+        data_manager: DataManager,
+        batch_size: int = 64,
+        test_batch_size: int = 8,
+        num_workers: int = 8
+    ):
+        super().__init__()
+        self.data_manager = data_manager
+        self.batch_size = batch_size
+        self.test_batch_size = test_batch_size
+        self.num_workers = num_workers
+        self.save_hyperparameters()
+
+    def setup(self, stage: str):
+        self.dataset_train = self.data_manager.train_iter_dataset
+        self.dataset_val = self.data_manager.val_iter_dataset
+        self.dataset_test = self.data_manager.test_iter_dataset
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.dataset_train,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            persistent_workers=True,
+            pin_memory=True
+        )
+
+    def val_dataloader(self):
+        val_dataloader = DataLoader(self.dataset_val, batch_size=self.test_batch_size, num_workers=1)
+        return val_dataloader
+
+    def test_dataloader(self):
+        return DataLoader(self.dataset_test, batch_size=self.test_batch_size, num_workers=1)
+
+    def predict_dataloader(self):
+        return DataLoader(self.dataset_test, batch_size=self.test_batch_size, num_workers=1)
\ No newline at end of file
diff --git a/probts/data/ltsf_datasets.py b/probts/data/ltsf_datasets.py
new file mode 100644
index 0000000..6ed740f
--- /dev/null
+++ b/probts/data/ltsf_datasets.py
@@ -0,0 +1,106 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from Autoformer
+# - Source: https://github.com/thuml/Autoformer/tree/main
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import os
+import pandas as pd
+from .time_features import time_features
+import numpy as np
+
+def get_LTSF_info(dataset):
+    if dataset == 'etth1' or dataset == 'etth2':
+        if dataset == 'etth1':
+            data_path = 'ETT-small/ETTh1.csv'
+        else:
+            data_path = 'ETT-small/ETTh2.csv'
+        freq = 'H'
+    elif dataset == 'ettm1' or dataset == 'ettm2':
+        if dataset == 'ettm1':
+            data_path = 'ETT-small/ETTm1.csv'
+        else:
+            data_path = 'ETT-small/ETTm2.csv'
+        freq = 'min'
+    elif dataset in ['traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'illness_ltsf', 'weather_ltsf']:
+        if dataset == 'traffic_ltsf':
+            data_path = 'traffic/traffic.csv'
+            freq = 'H'
+        elif dataset == 'electricity_ltsf':
+            data_path = 'electricity/electricity.csv'
+            freq = 'H'
+        elif dataset == 'exchange_ltsf':
+            data_path = 'exchange_rate/exchange_rate.csv'
+            freq = 'B'
+        elif dataset == 'illness_ltsf':
+            data_path = 'illness/national_illness.csv'
+            freq = 'W'
+        elif dataset == 'weather_ltsf':
+            data_path = 'weather/weather.csv'
+            freq = 'min'
+    elif dataset == 'caiso':
+        data_path = 'caiso/caiso_20130101_20210630.csv'
+        freq = 'H'
+    elif dataset == 'nordpool':
+        data_path = 'nordpool/production.csv'
+        freq = 'H'
+    else:
+        raise ValueError(f"Invalid dataset name: {dataset}!")
+    return data_path, freq
+
+def get_LTSF_borders(dataset, data_size):
+    if dataset == 'etth1' or dataset == 'etth2':
+        border_begin = [0, 12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24]
+        border_end = [12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24]
+    elif dataset == 'ettm1' or dataset == 'ettm2':
+        border_begin = [0, 12 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4]
+        border_end = [12 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 4 * 30 * 24 * 4, 12 * 30 * 24 * 4 + 8 * 30 * 24 * 4]
+    else:
+        num_train = int(data_size * 0.7)
+        num_test = int(data_size * 0.2)
+        num_vali = data_size - num_train - num_test
+        border_begin = [0, num_train, data_size - num_test]
+        border_end = [num_train, num_train + num_vali, data_size]
+    return border_begin, border_end
+
+def get_LTSF_Dataset(root_path, data_path,freq='h', timeenc=1):
+    if 'caiso' in data_path:
+        data = pd.read_csv(root_path + data_path)
+        data['Date'] = data['Date'].astype('datetime64[ns]')
+        names = ['PGE','SCE','SDGE','VEA','CA ISO','PACE','PACW','NEVP','AZPS','PSEI']
+        df_raw = pd.DataFrame(pd.date_range('20130101','20210630',freq='H')[:-1], columns=['Date'])
+        for name in names:
+            current_df = data[data['zone'] == name].drop_duplicates(subset='Date', keep='last').rename(columns={'load':name}).drop(columns=['zone'])
+            df_raw = df_raw.merge(current_df, on='Date', how='outer')
+        df_raw = df_raw.rename(columns={'Date': 'date'})
+    elif 'nordpool' in data_path:
+        df_raw = pd.read_csv(root_path + data_path, parse_dates=['Time'])
+        df_raw = df_raw.rename(columns={'Time': 'date'})
+    else:
+        df_raw = pd.read_csv(os.path.join(root_path, data_path))
+        
+    df_stamp = df_raw[['date']]
+    df_stamp['date'] = pd.to_datetime(df_stamp.date)
+    
+    if timeenc == 0:
+        df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
+        df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
+        df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
+        df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
+        df_stamp['minute'] = df_stamp.date.apply(lambda row: row.minute, 1)
+        df_stamp['minute'] = df_stamp.minute.map(lambda x: x // 15)
+        data_stamp = df_stamp.drop(labels='date', axis=1).values
+    elif timeenc == 1:
+        data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=freq)
+        data_stamp = data_stamp.transpose(1, 0)
+    elif timeenc == 2:
+        data_stamp = pd.to_datetime(df_stamp['date'].values)
+        data_stamp = np.array(data_stamp, dtype='datetime64[s]')
+
+    df_raw = df_raw.set_index(keys='date')
+    df_raw = df_raw.fillna(0)
+    target_dim = len(df_raw.columns)
+    data_size = len(df_raw)
+    return df_raw, data_stamp, target_dim, data_size
\ No newline at end of file
diff --git a/probts/data/stsf_datasets.py b/probts/data/stsf_datasets.py
new file mode 100644
index 0000000..d92c434
--- /dev/null
+++ b/probts/data/stsf_datasets.py
@@ -0,0 +1,193 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PyTorch-TS
+# - Source: https://github.com/zalandoresearch/pytorch-ts
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+from torch.utils.data import IterableDataset
+from gluonts.env import env
+from gluonts.dataset.common import Dataset
+from gluonts.itertools import Cyclic
+from gluonts.dataset.field_names import FieldName
+from gluonts.transform import (
+    SelectFields,
+    Transformation,
+    Chain,
+    InstanceSplitter,
+    ValidationSplitSampler,
+    TestSplitSampler,
+    ExpectedNumInstanceSampler,
+    RenameFields,
+    AsNumpyArray,
+    ExpandDimArray,
+    AddObservedValuesIndicator,
+    AddTimeFeatures,
+    VstackFeatures,
+    SetFieldIfNotPresent,
+    TargetDimIndicator,
+    TransformedDataset,
+)
+from .time_features import fourier_time_features_from_frequency, AddCustomizedTimeFeatures
+
+
+class GluonTSDataset():
+
+    def __init__(
+        self,
+        input_names: list,
+        history_length: int,
+        prediction_length: int,
+        freq: str,
+        multivariate: bool = True
+    ):
+        super().__init__()
+        self.input_names_ = input_names
+        self.history_length = history_length
+        self.prediction_length = prediction_length
+        self.freq = freq
+        if multivariate:
+            self.expected_ndim = 2
+        else:
+            self.expected_ndim = 1
+
+    def get_sampler(self):
+        self.train_sampler = ExpectedNumInstanceSampler(
+            num_instances=1.0,
+            min_past=self.history_length,
+            min_future=self.prediction_length,
+        )
+
+        self.val_sampler = ValidationSplitSampler(
+            min_past=self.history_length,
+            min_future=self.prediction_length,
+        )
+        
+        self.test_sampler = ValidationSplitSampler(
+            min_past=self.history_length,
+            min_future=self.prediction_length,
+        )
+
+
+    def create_transformation(self, data_stamp=None) -> Transformation:
+        
+        if data_stamp is None:
+            if self.freq in ["M", "W", "D", "B", "H", "min", "T"]:
+                time_features = fourier_time_features_from_frequency(self.freq)
+            else:
+                time_features = fourier_time_features_from_frequency('D')
+            self.time_feat_dim = len(time_features) * 2
+            time_feature_func = AddTimeFeatures
+        else:
+            self.time_feat_dim = data_stamp.shape[-1]
+            time_features = data_stamp
+            time_feature_func = AddCustomizedTimeFeatures
+
+        return Chain(
+            [
+                AsNumpyArray(
+                    field=FieldName.TARGET,
+                    expected_ndim=self.expected_ndim,
+                ),
+                ExpandDimArray(
+                    field=FieldName.TARGET,
+                    axis=None,
+                ),
+                AddObservedValuesIndicator(
+                    target_field=FieldName.TARGET,
+                    output_field=FieldName.OBSERVED_VALUES,
+                ),
+                time_feature_func(
+                    start_field=FieldName.START,
+                    target_field=FieldName.TARGET,
+                    output_field=FieldName.FEAT_TIME,
+                    time_features=time_features,
+                    pred_length=self.prediction_length,
+                ),
+                VstackFeatures(
+                    output_field=FieldName.FEAT_TIME,
+                    input_fields=[FieldName.FEAT_TIME],
+                ),
+                SetFieldIfNotPresent(field=FieldName.FEAT_STATIC_CAT, value=[0]),
+                TargetDimIndicator(
+                    field_name="target_dimension_indicator",
+                    target_field=FieldName.TARGET,
+                ),
+                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
+            ]
+        )
+
+    def create_instance_splitter(self, mode: str):
+        assert mode in ["train", "val", "test"]
+
+        self.get_sampler()
+        instance_sampler = {
+            "train": self.train_sampler,
+            "val": self.val_sampler,
+            "test": self.test_sampler,
+        }[mode]
+
+        return InstanceSplitter(
+            target_field=FieldName.TARGET,
+            is_pad_field=FieldName.IS_PAD,
+            start_field=FieldName.START,
+            forecast_start_field=FieldName.FORECAST_START,
+            instance_sampler=instance_sampler,
+            past_length=self.history_length,
+            future_length=self.prediction_length,
+            time_series_fields=[
+                FieldName.FEAT_TIME,
+                FieldName.OBSERVED_VALUES,
+            ],
+        ) + (
+            RenameFields(
+                {
+                    f"past_{FieldName.TARGET}": f"past_{FieldName.TARGET}_cdf",
+                    f"future_{FieldName.TARGET}": f"future_{FieldName.TARGET}_cdf",
+                }
+            )
+        )
+
+    def get_iter_dataset(self, dataset: Dataset, mode: str, data_stamp=None) -> IterableDataset:
+        assert mode in ["train", "val", "test"]
+
+        transform = self.create_transformation(data_stamp)
+        if mode == 'train':
+            with env._let(max_idle_transforms=100):
+                instance_splitter = self.create_instance_splitter(mode)
+        else:
+            instance_splitter = self.create_instance_splitter(mode)
+
+
+        input_names = self.input_names_
+
+        iter_dataset = TransformedIterableDataset(
+            dataset,
+            transform=transform
+            + instance_splitter
+            + SelectFields(input_names),
+            is_train=True if mode == 'train' else False
+        )
+
+        return iter_dataset
+
+
+
+class TransformedIterableDataset(IterableDataset):
+    def __init__(
+        self,
+        dataset: Dataset,
+        transform: Transformation,
+        is_train: bool = True
+    ):
+        super().__init__()
+
+        self.transformed_dataset = TransformedDataset(
+            Cyclic(dataset) if is_train else dataset,
+            transform,
+            is_train=is_train,
+        )
+
+    def __iter__(self):
+        return iter(self.transformed_dataset)
\ No newline at end of file
diff --git a/probts/data/time_features.py b/probts/data/time_features.py
new file mode 100644
index 0000000..cacf16a
--- /dev/null
+++ b/probts/data/time_features.py
@@ -0,0 +1,287 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from GluonTS
+# - Source: https://github.com/awslabs/gluonts
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+
+from typing import List
+
+import numpy as np
+import pandas as pd
+from pandas.tseries import offsets
+from pandas.tseries.frequencies import to_offset
+from gluonts.core.component import validated
+from gluonts.dataset.common import DataEntry
+from gluonts.transform import MapTransformation
+from typing import List, Type
+
+class TimeFeature:
+    def __init__(self):
+        pass
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        pass
+
+    def __repr__(self):
+        return self.__class__.__name__ + "()"
+
+
+class SecondOfMinute(TimeFeature):
+    """Minute of hour encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return index.second / 59.0 - 0.5
+
+
+class MinuteOfHour(TimeFeature):
+    """Minute of hour encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return index.minute / 59.0 - 0.5
+
+
+class HourOfDay(TimeFeature):
+    """Hour of day encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return index.hour / 23.0 - 0.5
+
+
+class DayOfWeek(TimeFeature):
+    """Hour of day encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return index.dayofweek / 6.0 - 0.5
+
+
+class DayOfMonth(TimeFeature):
+    """Day of month encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return (index.day - 1) / 30.0 - 0.5
+
+
+class DayOfYear(TimeFeature):
+    """Day of year encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return (index.dayofyear - 1) / 365.0 - 0.5
+
+
+class MonthOfYear(TimeFeature):
+    """Month of year encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return (index.month - 1) / 11.0 - 0.5
+
+
+class WeekOfYear(TimeFeature):
+    """Week of year encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return (index.isocalendar().week - 1) / 52.0 - 0.5
+
+
+def time_features_from_frequency_str(freq_str: str) -> List[TimeFeature]:
+    """
+    Returns a list of time features that will be appropriate for the given frequency string.
+    Parameters
+    ----------
+    freq_str
+        Frequency string of the form [multiple][granularity] such as "12H", "5min", "1D" etc.
+    """
+
+    features_by_offsets = {
+        offsets.YearEnd: [],
+        offsets.QuarterEnd: [MonthOfYear],
+        offsets.MonthEnd: [MonthOfYear],
+        offsets.Week: [DayOfMonth, WeekOfYear],
+        offsets.Day: [DayOfWeek, DayOfMonth, DayOfYear],
+        offsets.BusinessDay: [DayOfWeek, DayOfMonth, DayOfYear],
+        offsets.Hour: [HourOfDay, DayOfWeek, DayOfMonth, DayOfYear],
+        offsets.Minute: [
+            MinuteOfHour,
+            HourOfDay,
+            DayOfWeek,
+            DayOfMonth,
+            DayOfYear,
+        ],
+        offsets.Second: [
+            SecondOfMinute,
+            MinuteOfHour,
+            HourOfDay,
+            DayOfWeek,
+            DayOfMonth,
+            DayOfYear,
+        ],
+    }
+
+    offset = to_offset(freq_str)
+
+    for offset_type, feature_classes in features_by_offsets.items():
+        if isinstance(offset, offset_type):
+            return [cls() for cls in feature_classes]
+
+    supported_freq_msg = f"""
+    Unsupported frequency {freq_str}
+    The following frequencies are supported:
+        Y   - yearly
+            alias: A
+        M   - monthly
+        W   - weekly
+        D   - daily
+        B   - business days
+        H   - hourly
+        T   - minutely
+            alias: min
+        S   - secondly
+    """
+    raise RuntimeError(supported_freq_msg)
+
+
+def time_features(dates, freq='h'):
+    return np.vstack([feat(dates) for feat in time_features_from_frequency_str(freq)])
+
+
+
+class FourierDateFeatures(TimeFeature):
+    def __init__(self, freq: str) -> None:
+        super().__init__()
+        # reocurring freq
+        freqs = [
+            "month",
+            "day",
+            "hour",
+            "minute",
+            "weekofyear",
+            "weekday",
+            "dayofweek",
+            "dayofyear",
+            "daysinmonth",
+        ]
+
+        assert freq in freqs
+        self.freq = freq
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        values = getattr(index, self.freq)
+        num_values = max(values) + 1
+        steps = [x * 2.0 * np.pi / num_values for x in values]
+        return np.vstack([np.cos(steps), np.sin(steps)])
+
+
+def norm_freq_str(freq_str: str) -> str:
+    base_freq = freq_str.split("-")[0]
+
+    # Pandas has start and end frequencies, e.g `AS` and `A` for yearly start
+    # and yearly end frequencies. We don't make that difference and instead
+    # rely only on the end frequencies which don't have the `S` prefix.
+    # Note: Secondly ("S") frequency exists, where we don't want to remove the
+    # "S"!
+    if len(base_freq) >= 2 and base_freq.endswith("S"):
+        return base_freq[:-1]
+
+    return base_freq
+
+
+def fourier_time_features_from_frequency(freq_str: str) -> List[TimeFeature]:
+    offset = to_offset(freq_str)
+    granularity = norm_freq_str(offset.name)
+    granularity = granularity.upper()
+    features = {
+        "M": ["weekofyear"],
+        "W": ["daysinmonth", "weekofyear"],
+        "D": ["dayofweek"],
+        "B": ["dayofweek", "dayofyear"],
+        "H": ["hour", "dayofweek"],
+        "min": ["minute", "hour", "dayofweek"],
+        "T": ["minute", "hour", "dayofweek"],
+    }
+
+    assert granularity in features, f"freq {granularity} not supported"
+
+    feature_classes: List[TimeFeature] = [
+        FourierDateFeatures(freq=freq) for freq in features[granularity]
+    ]
+    return feature_classes
+
+
+def get_lags(freq_str:str):
+    freq_str = freq_str.upper()
+    if freq_str == "M":
+        lags = [1, 12]
+    elif freq_str == "D":
+        lags = [1, 7, 14]
+    elif freq_str == "B":
+        lags = [1, 2]
+    elif freq_str == "H":
+        lags = [1, 24, 168]
+    elif freq_str in ("T", "min"):
+        lags = [1, 4, 12, 24, 48]
+    else:
+        lags = [1]
+
+    return lags
+
+
+def target_transformation_length(
+    target: np.ndarray, pred_length: int, is_train: bool
+) -> int:
+    return target.shape[-1] + (0 if is_train else pred_length)
+
+
+class AddCustomizedTimeFeatures(MapTransformation):
+    """
+    Adds a set of time features.
+
+    If `is_train=True` the feature matrix has the same length as the `target`
+    field. If `is_train=False` the feature matrix has length
+    `len(target) + pred_length`
+
+    Parameters
+    ----------
+    start_field
+        Field with the start time stamp of the time series
+    target_field
+        Field with the array containing the time series values
+    output_field
+        Field name for result.
+    time_features
+        list of time features to use.
+    pred_length
+        Prediction length
+    """
+
+    @validated()
+    def __init__(
+        self,
+        start_field: str,
+        target_field: str,
+        output_field: str,
+        time_features,
+        pred_length: int,
+        dtype: Type = np.float32,
+    ) -> None:
+        self.date_features = time_features
+        self.pred_length = pred_length
+        self.start_field = start_field
+        self.target_field = target_field
+        self.output_field = output_field
+        self.dtype = dtype
+
+    def map_transform(self, data: DataEntry, is_train: bool) -> DataEntry:
+        length = target_transformation_length(
+            data[self.target_field], self.pred_length, is_train=is_train
+        )
+
+        if len(self.date_features.shape) == 2:
+            data[self.output_field] = self.date_features[:length].astype(self.dtype)
+        else:
+            data[self.output_field] = self.date_features[:length].astype(np.float64)
+        data[self.output_field] = self.date_features[:length].astype(np.float64)
+        data[self.output_field] = np.transpose(data[self.output_field])
+        
+        return data
diff --git a/probts/model/__init__.py b/probts/model/__init__.py
new file mode 100644
index 0000000..5330601
--- /dev/null
+++ b/probts/model/__init__.py
@@ -0,0 +1 @@
+from .forecast_module import *
\ No newline at end of file
diff --git a/probts/model/forecast_module.py b/probts/model/forecast_module.py
new file mode 100644
index 0000000..1ff028f
--- /dev/null
+++ b/probts/model/forecast_module.py
@@ -0,0 +1,146 @@
+import numpy as np
+import torch
+from torch import optim
+from typing import Dict
+import lightning.pytorch as pl
+import sys
+import importlib
+from probts.data import ProbTSBatchData
+from probts.model.forecaster import Forecaster
+from probts.utils import Evaluator, Scaler
+
+class ProbTSForecastModule(pl.LightningModule):
+    def __init__(
+        self,
+        forecaster: Forecaster,
+        scaler: Scaler = None,
+        num_samples: int = 100,
+        learning_rate: float = 1e-3,
+        quantiles_num: int = 10,
+        load_from_ckpt: str = None,
+        **kwargs
+    ):
+        super().__init__()
+        self.num_samples = num_samples
+        self.learning_rate = learning_rate
+        self.load_from_ckpt = load_from_ckpt
+        
+        self.forecaster = forecaster
+
+        self.scaler = scaler
+        self.evaluator = Evaluator(quantiles_num=quantiles_num)
+        self.save_hyperparameters()
+        
+    @classmethod
+    def load_from_checkpoint(self, checkpoint_path, scaler=None, learning_rate=None, **kwargs):
+        # Load the checkpoint
+        checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
+        # Extract the arguments for the forecaster
+        forecaster_args = checkpoint['hyper_parameters']['forecaster']
+
+        if isinstance(forecaster_args, Forecaster):
+            forecaster = forecaster_args
+        else:
+            module_path, class_name = forecaster_args['class_path'].rsplit('.', 1)
+            forecaster_class = getattr(importlib.import_module(module_path), class_name)
+            
+            # Add any missing required arguments
+            forecaster_args = forecaster_args['init_args']
+            forecaster_args.update(kwargs)
+            
+            # Create the forecaster
+            forecaster = forecaster_class(**forecaster_args)
+        
+        if learning_rate is None:
+            learning_rate = checkpoint['hyper_parameters'].get('learning_rate', 1e-3)
+        
+        # Create the model instance
+        model = self(
+            forecaster=forecaster,
+            scaler=scaler,
+            num_samples=checkpoint['hyper_parameters'].get('num_samples', 100),
+            learning_rate=learning_rate,
+            quantiles_num=checkpoint['hyper_parameters'].get('quantiles_num', 10),
+            load_from_ckpt=checkpoint['hyper_parameters'].get('load_from_ckpt', None),
+            **kwargs  # Pass additional arguments here
+        )
+        model.load_state_dict(checkpoint['state_dict'])
+        return model
+
+    def training_forward(self, batch_data):
+        batch_data.past_target_cdf = self.scaler.transform(batch_data.past_target_cdf)
+        batch_data.future_target_cdf = self.scaler.transform(batch_data.future_target_cdf)
+        loss = self.forecaster.loss(batch_data)
+        return loss
+
+    def training_step(self, batch, batch_idx):
+        batch_data = ProbTSBatchData(batch, self.device)
+        loss = self.training_forward(batch_data)
+        self.log("train_loss", loss, on_step=True, prog_bar=True, logger=True)
+        return loss
+
+    def evaluate(self, batch, stage=''):
+        batch_data = ProbTSBatchData(batch, self.device)
+        self.batch_size.append(batch_data.past_target_cdf.shape[0])
+        
+        # Forecast
+        batch_data.past_target_cdf = self.scaler.transform(batch_data.past_target_cdf)
+        forecasts = self.forecaster.forecast(batch_data, self.num_samples)
+        
+        # Calculate denorm metrics
+        denorm_forecasts = self.scaler.inverse_transform(forecasts)
+        metrics = self.evaluator(batch_data.future_target_cdf, denorm_forecasts)
+        self.update_metrics(metrics, stage)
+        
+        # Calculate norm metrics
+        norm_future_target_cdf = self.scaler.transform(batch_data.future_target_cdf)
+        norm_metrics = self.evaluator(norm_future_target_cdf, forecasts)
+        self.update_metrics(norm_metrics, stage, 'norm')
+        return metrics
+
+    def validation_step(self, batch, batch_idx, dataloader_idx=None):
+        metrics = self.evaluate(batch, stage='val')
+        return metrics
+
+    def on_validation_epoch_start(self):
+        self.metrics_dict = {}
+        self.batch_size = []
+
+    def on_validation_epoch_end(self):
+        avg_metrics = self.calculate_weighted_average(self.metrics_dict, self.batch_size)
+        self.log_dict(avg_metrics, prog_bar=True)
+
+    def test_step(self, batch, batch_idx):
+        metrics = self.evaluate(batch, stage='test')
+        return metrics
+
+    def on_test_epoch_start(self):
+        self.metrics_dict = {}
+        self.batch_size = []
+
+    def on_test_epoch_end(self):
+        avg_metrics = self.calculate_weighted_average(self.metrics_dict, self.batch_size)
+        self.log_dict(avg_metrics, logger=True)
+
+    def predict_step(self, batch, batch_idx):
+        batch_data = ProbTSBatchData(batch, self.device)
+        forecasts = self.forecaster.forecast(batch_data, self.num_samples)
+        return forecasts
+
+    def configure_optimizers(self):
+        optimizer = optim.Adam(self.parameters(), lr=self.learning_rate)
+        return optimizer
+
+    def update_metrics(self, new_metrics: Dict, stage: str, key: str = ''):
+        prefix = stage if key == '' else f'{stage}_{key}'
+        for metric_name, metric_value in new_metrics.items():
+            metric_key = f'{prefix}_{metric_name}'
+            if metric_key not in self.metrics_dict:
+                self.metrics_dict[metric_key] = []
+            self.metrics_dict[metric_key].append(metric_value)
+    
+    def calculate_weighted_average(self, metrics_dict: Dict, batch_size: list):
+        metrics = {}
+        for key, value in metrics_dict.items():
+            metrics[key] = np.sum(value * np.array(batch_size)) / np.sum(batch_size)
+        return metrics
diff --git a/probts/model/forecaster/__init__.py b/probts/model/forecaster/__init__.py
new file mode 100644
index 0000000..98def69
--- /dev/null
+++ b/probts/model/forecaster/__init__.py
@@ -0,0 +1,3 @@
+from .forecaster import Forecaster
+from .point_forecaster import *
+from .prob_forecaster import *
\ No newline at end of file
diff --git a/probts/model/forecaster/forecaster.py b/probts/model/forecaster/forecaster.py
new file mode 100644
index 0000000..7d5706f
--- /dev/null
+++ b/probts/model/forecaster/forecaster.py
@@ -0,0 +1,169 @@
+import torch
+from torch import nn
+from typing import List
+
+from probts.utils import weighted_average, TemporalScaler
+
+
+class Forecaster(nn.Module):
+    def __init__(
+        self,
+        target_dim: int,
+        context_length: int,
+        prediction_length: int,
+        freq: str ,
+        use_lags: bool = False,
+        use_feat_idx_emb: bool = False,
+        use_time_feat: bool = False,
+        lags_list: List[int] = None,
+        feat_idx_emb_dim: int = 1,
+        time_feat_dim: int = 1,
+        use_scaling: bool = False,
+        autoregressive: bool = False,
+        no_training: bool = False,
+        **kwargs
+    ):
+        super().__init__()
+        
+        self.context_length = context_length
+        self.prediction_length = prediction_length
+        self.target_dim = target_dim
+        self.freq = freq
+        self.use_lags = use_lags
+        self.use_feat_idx_emb = use_feat_idx_emb
+        self.use_time_feat = use_time_feat
+        self.feat_idx_emb_dim = feat_idx_emb_dim
+        self.time_feat_dim = time_feat_dim
+        self.autoregressive = autoregressive
+        self.no_training = no_training
+        self.use_scaling = use_scaling
+        # Lag parameters
+        self.lags_list = lags_list
+        if self.use_scaling:
+            self.scaler = TemporalScaler()
+        else:
+            self.scaler = None
+        
+        self.lags_dim = len(self.lags_list) * target_dim
+        self.feat_idx_emb = nn.Embedding(
+            num_embeddings=self.target_dim, embedding_dim=self.feat_idx_emb_dim
+        )
+        self.input_size = self.get_input_size()
+            
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def get_input_size(self):
+        input_size = self.target_dim if not self.use_lags else self.lags_dim
+        if self.use_feat_idx_emb:
+            input_size += self.use_feat_idx_emb * self.target_dim
+        if self.use_time_feat:
+            input_size += self.time_feat_dim
+        return input_size
+
+    def get_lags(self, sequence, lags_list, lags_length=1):
+        """
+        Get several lags from the sequence of shape (B, L, C) to (B, L', C*N),
+        where L' = lag_length and N = len(lag_list).
+        """
+        assert max(lags_list) + lags_length <= sequence.shape[1]
+
+        lagged_values = []
+        for lag_index in lags_list:
+            begin_index = -lag_index - lags_length
+            end_index = -lag_index if lag_index > 0 else None
+            lagged_value = sequence[:, begin_index:end_index, ...]
+            if self.use_scaling:
+                lagged_value = lagged_value / self.scaler.scale
+            lagged_values.append(lagged_value)
+        return torch.cat(lagged_values, dim=-1)
+
+    def get_input_sequence(
+        self,
+        past_target_cdf,
+        future_target_cdf,
+        mode
+    ):
+        if mode == 'all':
+            sequence = torch.cat((past_target_cdf, future_target_cdf), dim=1)
+            seq_length = self.context_length + self.prediction_length
+        elif mode == 'encode':
+            sequence = past_target_cdf
+            seq_length = self.context_length
+        elif mode == 'decode':
+            sequence = past_target_cdf
+            seq_length = 1
+        else:
+            raise ValueError(f"Unsupported input mode: {mode}")
+        
+        if self.use_lags:
+            input_seq = self.get_lags(sequence, self.lags_list, seq_length)
+        else: 
+            input_seq = sequence[:, -seq_length:, ...]
+            if self.use_scaling:
+                input_seq = input_seq / self.scaler.scale
+        return input_seq
+    
+    def get_input_feat_idx_emb(self, target_dimension_indicator, input_length):
+        input_feat_idx_emb = self.feat_idx_emb(target_dimension_indicator) # [B K D]
+
+        input_feat_idx_emb = (
+            input_feat_idx_emb.unsqueeze(1)
+            .expand(-1, input_length, -1, -1)
+            .reshape(-1, input_length, self.target_dim * self.feat_idx_emb_dim)
+        )
+        return input_feat_idx_emb # [B L K*D]
+
+    def get_input_time_feat(
+        self,
+        past_time_feat,
+        future_time_feat,
+        mode
+    ):
+        if mode == 'all':
+            time_feat = torch.cat(
+                (past_time_feat[:, -self.context_length:, ...], future_time_feat), dim=1)
+        elif mode == 'encode':
+            time_feat = past_time_feat[:, -self.context_length:, ...]
+        elif mode == 'decode':
+            time_feat = future_time_feat
+        return time_feat
+
+    def get_inputs(self, batch_data, mode):
+        inputs_list = []
+
+        input_seq = self.get_input_sequence(
+            batch_data.past_target_cdf, batch_data.future_target_cdf, mode=mode)
+        input_length = input_seq.shape[1] # [B L n_lags*K]
+        inputs_list.append(input_seq)
+
+        if self.use_feat_idx_emb:
+            input_feat_idx_emb = self.get_input_feat_idx_emb(
+                batch_data.target_dimension_indicator, input_length) # [B L K*D]
+            inputs_list.append(input_feat_idx_emb)
+
+        if self.use_time_feat:
+            input_time_feat = self.get_input_time_feat(
+                batch_data.past_time_feat, batch_data.future_time_feat, mode=mode) # [B L Dt]
+            inputs_list.append(input_time_feat)
+        return torch.cat(inputs_list, dim=-1).to(dtype=torch.float32)
+    
+    def get_scale(self, batch_data):
+        self.scaler.fit(
+            batch_data.past_target_cdf[:, -self.context_length:, ...],
+            batch_data.past_observed_values[:, -self.context_length:, ...]
+        )
+    
+    def get_weighted_loss(self, batch_data, loss):
+        observed_values =  batch_data.future_observed_values
+        loss_weights, _ = observed_values.min(dim=-1, keepdim=True)
+        loss = weighted_average(loss, weights=loss_weights, dim=1)
+        return loss
+    
+    def loss(self, batch_data):
+        raise NotImplementedError
+    
+    def forecast(self, batch_data=None, num_samples=None):
+        raise NotImplementedError
diff --git a/probts/model/forecaster/point_forecaster/__init__.py b/probts/model/forecaster/point_forecaster/__init__.py
new file mode 100644
index 0000000..63fa456
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/__init__.py
@@ -0,0 +1,42 @@
+from .mean import MeanForecaster
+from .linear import LinearForecaster
+from .patchtst import PatchTST
+from .transformer import TransformerForecaster
+from .gru import GRUForecaster
+from .dlinear import DLinear
+from .nlinear import NLinear
+from .nhits import NHiTS
+from .timesnet import TimesNet
+from .itransformer import iTransformer
+from .autoformer import Autoformer
+
+# ------- add timesfm to sys.path ----------
+try:
+    import os, sys
+    current_dir = os.path.dirname(os.path.realpath(__file__))
+    project_root = os.path.abspath(os.path.join(current_dir, '..', '..', '..', '..'))
+    timesfm_path = os.path.join(project_root, 'submodules', 'timesfm', 'src')
+
+    if timesfm_path not in sys.path:
+        sys.path.append(timesfm_path)
+except Exception as e:
+    print(f"Warning: Unable to add timesfm to sys.path. {e}")
+# ------------------------------------------
+
+import importlib
+
+modules = [
+    ('timesfm', 'TimesFM'),
+    ('timer', 'Timer'),
+    ('units', 'UniTS'),
+    ('forecastpfn', 'ForecastPFN'),
+    ('tinytimemixer', 'TinyTimeMixer')
+]
+
+for module, class_name in modules:
+    try:
+        mod = importlib.import_module(f".{module}", package=__package__)
+        globals()[class_name] = getattr(mod, class_name)
+    except ImportError:
+        # print(f"Warning: {class_name} is not available due to missing dependencies.")
+        pass
\ No newline at end of file
diff --git a/probts/model/forecaster/point_forecaster/autoformer.py b/probts/model/forecaster/point_forecaster/autoformer.py
new file mode 100644
index 0000000..01571fc
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/autoformer.py
@@ -0,0 +1,139 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from Autoformer
+# - Source: https://github.com/thuml/Autoformer
+# - Paper: Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
+# - License: MIT License
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+from probts.model.forecaster import Forecaster
+from probts.model.nn.layers.Embed import DataEmbedding_wo_pos
+from probts.model.nn.layers.AutoCorrelation import AutoCorrelation, AutoCorrelationLayer
+from probts.model.nn.layers.Autoformer_EncDec import Encoder, Decoder, EncoderLayer, DecoderLayer, my_Layernorm, series_decomp
+
+
+class Autoformer(Forecaster):
+    def __init__(
+        self,
+        moving_avg: int = 25,
+        factor: int = 1,
+        n_heads: int = 8,
+        activation: str = 'gelu',
+        e_layers: int = 2,
+        d_layers: int = 1,
+        output_attention: bool = False,
+        d_ff: int = 512,
+        label_len: int = 48,
+        embed: str = 'timeF',
+        dropout: float = 0.1,
+        f_hidden_size: int = 512,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        self.label_len = self.context_length
+
+        # Decomp
+        kernel_size = moving_avg
+        self.decomp = series_decomp(kernel_size)
+
+        # Embedding
+        # The series-wise connection inherently contains the sequential information.
+        # Thus, we can discard the position embedding of transformers.
+        self.enc_embedding = DataEmbedding_wo_pos(self.target_dim, f_hidden_size, embed, self.freq.lower(),
+                                                  dropout)
+        self.dec_embedding = DataEmbedding_wo_pos(self.target_dim, f_hidden_size, embed, self.freq.lower(),
+                                                  dropout)
+
+        # Encoder
+        self.model_encoder = Encoder(
+            [
+                EncoderLayer(
+                    AutoCorrelationLayer(
+                        AutoCorrelation(False, factor, attention_dropout=dropout,
+                                        output_attention=output_attention),
+                        f_hidden_size, n_heads),
+                    f_hidden_size,
+                    d_ff,
+                    moving_avg=moving_avg,
+                    dropout=dropout,
+                    activation=activation
+                ) for l in range(e_layers)
+            ],
+            norm_layer=my_Layernorm(f_hidden_size)
+        )
+        
+        # Decoder
+        self.model_decoder = Decoder(
+            [
+                DecoderLayer(
+                    AutoCorrelationLayer(
+                        AutoCorrelation(True, factor, attention_dropout=dropout,
+                                        output_attention=False),
+                        f_hidden_size, n_heads),
+                    AutoCorrelationLayer(
+                        AutoCorrelation(False, factor, attention_dropout=dropout,
+                                        output_attention=False),
+                        f_hidden_size, n_heads),
+                    f_hidden_size,
+                    self.target_dim,
+                    d_ff,
+                    moving_avg=moving_avg,
+                    dropout=dropout,
+                    activation=activation,
+                )
+                for l in range(d_layers)
+            ],
+            norm_layer=my_Layernorm(f_hidden_size),
+            projection=nn.Linear(f_hidden_size, self.target_dim, bias=True)
+        )
+        self.loss_fn = nn.MSELoss()
+        
+    def forward(self, inputs, enc_self_mask=None, dec_self_mask=None, dec_enc_mask=None, *args, **kwargs):
+        B, _, _ = inputs.shape
+
+        if self.use_time_feat:
+            past_target = inputs[:,:self.context_length, :self.target_dim]
+            x_mark_enc = inputs[:,:self.context_length, self.target_dim:]
+            time_feat = inputs[:,:,self.target_dim:]
+        else:
+            past_target = inputs[:,:self.context_length,:self.target_dim]
+            x_mark_enc = None
+            time_feat = None
+            
+        
+        # decomp init
+        mean = torch.mean(past_target, dim=1).unsqueeze(1).repeat(1, self.prediction_length, 1)
+        zeros = torch.zeros([B, self.prediction_length, self.target_dim], device=past_target.device)
+        seasonal_init, trend_init = self.decomp(past_target)
+        # decoder input
+        trend_init = torch.cat([trend_init[:, -self.label_len:, :], mean], dim=1)
+        seasonal_init = torch.cat([seasonal_init[:, -self.label_len:, :], zeros], dim=1)
+
+        enc_out = self.enc_embedding(past_target, x_mark_enc)
+        enc_out, attns = self.model_encoder(enc_out, attn_mask=enc_self_mask)
+        # dec
+        dec_out = self.dec_embedding(seasonal_init, time_feat)
+        seasonal_part, trend_part = self.model_decoder(dec_out, enc_out, x_mask=dec_self_mask, cross_mask=dec_enc_mask,
+                                                 trend=trend_init)
+        # final
+        dec_out = trend_part + seasonal_part
+        return dec_out[:, -self.prediction_length:, :]
+
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'all')
+        outputs = self(inputs)
+        
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'all')
+
+        outputs = self(inputs)
+        return outputs.unsqueeze(1)
\ No newline at end of file
diff --git a/probts/model/forecaster/point_forecaster/dlinear.py b/probts/model/forecaster/point_forecaster/dlinear.py
new file mode 100644
index 0000000..158ecb5
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/dlinear.py
@@ -0,0 +1,114 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from LTSF-Linear
+# - Source: https://github.com/cure-lab/LTSF-Linear
+# - Paper: Are Transformers Effective for Time Series Forecasting?
+# - License: Apache-2.0
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+from probts.model.forecaster import Forecaster
+import sys
+
+class moving_avg(nn.Module):
+    """
+    Moving average block to highlight the trend of time series
+    """
+    def __init__(self, kernel_size, stride):
+        super(moving_avg, self).__init__()
+        self.kernel_size = kernel_size
+        self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)
+
+    def forward(self, x):
+        # padding on the both ends of time series
+        front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)
+        end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
+        x = torch.cat([front, x, end], dim=1)
+        x = self.avg(x.permute(0, 2, 1))
+        x = x.permute(0, 2, 1)
+        return x
+
+
+class series_decomp(nn.Module):
+    """
+    Series decomposition block
+    """
+    def __init__(self, kernel_size):
+        super(series_decomp, self).__init__()
+        self.moving_avg = moving_avg(kernel_size, stride=1)
+
+    def forward(self, x):
+        moving_mean = self.moving_avg(x)
+        res = x - moving_mean
+        return res, moving_mean
+
+
+class DLinear(Forecaster):
+    def __init__(
+        self,
+        kernel_size: int,
+        individual: bool,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        if self.input_size != self.target_dim:
+            self.enc_linear = nn.Linear(
+                in_features=self.input_size, out_features=self.target_dim
+            )
+        else:
+            self.enc_linear = nn.Identity()
+
+
+        # Decompsition Kernel Size
+        self.kernel_size = kernel_size
+        self.decompsition = series_decomp(kernel_size)
+        self.individual = individual
+
+        if self.individual:
+            self.Linear_Seasonal = nn.ModuleList()
+            self.Linear_Trend = nn.ModuleList()
+            
+            for i in range(self.target_dim):
+                self.Linear_Seasonal.append(nn.Linear(self.context_length, self.prediction_length))
+                self.Linear_Trend.append(nn.Linear(self.context_length, self.prediction_length))
+        else:
+            self.Linear_Seasonal = nn.Linear(self.context_length, self.prediction_length)
+            self.Linear_Trend = nn.Linear(self.context_length, self.prediction_length)
+        self.loss_fn = nn.MSELoss()
+
+    def encoder(self, inputs):
+        seasonal_init, trend_init = self.decompsition(inputs)
+
+        # [B,C,L]
+        seasonal_init, trend_init = seasonal_init.permute(0,2,1), trend_init.permute(0,2,1)
+
+        if self.individual:
+            seasonal_output = torch.zeros([seasonal_init.size(0),seasonal_init.size(1),self.prediction_length],dtype=seasonal_init.dtype).to(seasonal_init.device)
+            trend_output = torch.zeros([trend_init.size(0),trend_init.size(1),self.prediction_length],dtype=trend_init.dtype).to(trend_init.device)
+            for i in range(self.target_dim):
+                seasonal_output[:,i,:] = self.Linear_Seasonal[i](seasonal_init[:,i,:])
+                trend_output[:,i,:] = self.Linear_Trend[i](trend_init[:,i,:])
+        else:
+            seasonal_output = self.Linear_Seasonal(seasonal_init)
+            trend_output = self.Linear_Trend(trend_init)
+
+        outputs = seasonal_output + trend_output # [B,C,L]
+        return outputs.permute(0,2,1)
+
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = self.enc_linear(inputs)
+        outputs = self.encoder(inputs)
+        
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = self.enc_linear(inputs)
+        outputs = self.encoder(inputs)
+        return outputs.unsqueeze(1)
diff --git a/probts/model/forecaster/point_forecaster/forecastpfn.py b/probts/model/forecaster/point_forecaster/forecastpfn.py
new file mode 100644
index 0000000..0223205
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/forecastpfn.py
@@ -0,0 +1,167 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from ForecastPFN
+# - Source: https://github.com/abacusai/ForecastPFN
+# - Paper: ForecastPFN: Synthetically-Trained Zero-Shot Forecasting
+# - License: Apache License 2.0
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import datetime
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import torch
+from keras import backend
+from sklearn.preprocessing import StandardScaler
+
+from probts.model.forecaster import Forecaster
+
+
+def smape(y_true, y_pred):
+    """ Calculate Armstrong's original definition of sMAPE between `y_true` & `y_pred`.
+        `loss = 200 * mean(abs((y_true - y_pred) / (y_true + y_pred), axis=-1)`
+        Args:
+        y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`.
+        y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.
+        Returns:
+        Symmetric mean absolute percentage error values. shape = `[batch_size, d0, ..
+        dN-1]`.
+        """
+    y_pred = tf.convert_to_tensor(y_pred)
+    y_true = tf.cast(y_true, y_pred.dtype)
+    diff = tf.abs(
+        (y_true - y_pred) /
+        backend.maximum(y_true + y_pred, backend.epsilon())
+    )
+    return 200.0 * backend.mean(diff, axis=-1)
+
+
+class ForecastPFN(Forecaster):
+    def __init__(
+        self,
+        label_len: int = 48,
+        ckpt_path: str = None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.no_training = True
+
+        self.label_len = label_len
+        
+        self.model = tf.keras.models.load_model(ckpt_path, custom_objects={'smape': smape})
+
+
+    def _ForecastPFN_time_features(self, x_mark_enc: np.ndarray, x_mark_dec: np.ndarray):
+        def extract_time_features(ts):
+            original_shape = ts.shape
+            ts = ts.reshape(-1)  # Flatten the array
+            if type(ts[0]) == datetime.datetime:
+                year = np.array([x.year for x in ts])
+                month = np.array([x.month for x in ts])
+                day = np.array([x.day for x in ts])
+                day_of_week = np.array([x.weekday() + 1 for x in ts])
+                day_of_year = np.array([x.timetuple().tm_yday for x in ts])
+            else:
+                ts = pd.to_datetime(ts)
+                year = ts.year.values
+                month = ts.month.values
+                day = ts.day.values
+                day_of_week = ts.day_of_week.values + 1
+                day_of_year = ts.day_of_year.values
+            
+            features = np.stack([year, month, day, day_of_week, day_of_year], axis=-1)
+            return features.reshape(*original_shape, -1).squeeze()
+
+        # Process the encoder and decoder inputs
+        x_mark_enc_features = extract_time_features(x_mark_enc)
+        x_mark_dec_features = extract_time_features(x_mark_dec)
+
+        return x_mark_enc_features, x_mark_dec_features
+
+    def _process_tuple(self, x, x_mark, y_mark, horizon):
+        """
+        x: tensor of shape (n, 1)
+        x_mark: tensor of shape (n, d)
+        y_mark: tensor of shape (horizon, d)
+
+        where
+        n       is the input sequence length
+        horizon is the output sequence length
+        d is the dimensionality of the time_stamp (5 for ForecastPFN)
+        """
+        if tf.reduce_all(x == x[0]):
+            x = tf.concat([x[:-1], x[-1:] + 1], axis=0)
+        
+        history = x.numpy()
+        scaler = StandardScaler()
+        scaler.fit(history)
+        history = scaler.transform(history)
+        
+        history_mean = np.nanmean(history[-6:])
+        history_std = np.nanstd(history[-6:])
+        local_scale = history_mean + history_std + 1e-4
+        
+        history = np.clip(history / local_scale, a_min=0, a_max=1)
+        
+        if x.shape[0] != 100:
+            if x.shape[0] > 100:
+                target = x_mark[-100:, :]
+                history = history[-100:, :]
+            else:
+                target = tf.pad(x_mark, [[100 - x.shape[0], 0], [0, 0]])
+                history = tf.pad(history, [[100 - x.shape[0], 0], [0, 0]])
+            
+            history = tf.repeat(tf.expand_dims(history, axis=0), horizon, axis=0)[:, :, 0]
+            ts = tf.repeat(tf.expand_dims(target, axis=0), horizon, axis=0)
+        else:
+            ts = tf.repeat(tf.expand_dims(x_mark, axis=0), horizon, axis=0)
+            history = tf.convert_to_tensor(history, dtype=tf.float32)
+        
+        task = tf.fill([horizon], 1)
+        y_mark_tensor = tf.convert_to_tensor(y_mark[-horizon:, :], dtype=tf.int64)
+        target_ts = tf.expand_dims(y_mark_tensor, axis=1)
+        
+        model_input = {'ts': ts, 'history': history, 'target_ts': target_ts, 'task': task}
+        pred_vals = self.model(model_input)
+        
+        scaled_vals = pred_vals['result'].numpy().T.reshape(-1) * pred_vals['scale'].numpy().reshape(-1)
+        scaled_vals = scaler.inverse_transform([scaled_vals])
+        return scaled_vals
+    
+    
+    def _process_batch(self, batch_x, batch_y, batch_x_mark, batch_y_mark):
+        preds = []
+        for idx, (x, y, x_mark, y_mark) in enumerate(zip(batch_x, batch_y, batch_x_mark, batch_y_mark)):
+            pred = self._process_tuple(x, x_mark, y_mark, self.prediction_length)
+            preds.append(pred)
+        return preds
+
+
+    def forecast(self, batch_data, num_samples=None):
+        # For now, we only support batch_size=1
+        B, _, K = batch_data.past_target_cdf.shape
+        inputs = batch_data.past_target_cdf[:, -self.context_length:, ...].cpu()
+        x_mark_enc = batch_data.past_time_feat[:, -self.context_length:, ...].cpu().numpy().astype('datetime64[s]')
+        x_mark_dec = batch_data.future_time_feat.cpu().numpy().astype('datetime64[s]')
+        x_mark_enc, x_mark_dec = self._ForecastPFN_time_features(x_mark_enc, x_mark_dec)
+
+        x_mark_dec = tf.concat([x_mark_enc[:, -self.label_len:, :], x_mark_dec], axis=1)
+        
+        inputs = tf.reshape(inputs, [-1, self.context_length, 1])
+        x_mark_enc = tf.repeat(x_mark_enc, repeats=K, axis=0)
+        x_mark_dec = tf.repeat(x_mark_dec, repeats=K, axis=0)
+        
+        dec_inp = tf.zeros_like(inputs[:, -self.prediction_length:, :])
+        dec_inp = tf.concat([inputs[:, -self.label_len:, :], dec_inp], axis=1)
+        x_mark_enc = tf.cast(x_mark_enc, tf.int64)
+        x_mark_dec = tf.cast(x_mark_dec, tf.int64)
+        
+        outputs = self._process_batch(inputs, dec_inp, x_mark_enc, x_mark_dec)
+        outputs = tf.concat(outputs, axis=0)
+        outputs = tf.reshape(outputs, [B, -1, K])
+        outputs = outputs[:, :self.prediction_length, :].numpy()
+        outputs = torch.tensor(outputs)
+        return outputs.unsqueeze(1)
\ No newline at end of file
diff --git a/probts/model/forecaster/point_forecaster/gru.py b/probts/model/forecaster/point_forecaster/gru.py
new file mode 100644
index 0000000..6774d28
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/gru.py
@@ -0,0 +1,72 @@
+import torch
+import torch.nn as nn
+
+from probts.data import ProbTSBatchData
+from probts.utils import repeat
+from probts.model.forecaster import Forecaster
+
+
+class GRUForecaster(Forecaster):
+    def __init__(
+        self,
+        num_layers: int = 2,
+        f_hidden_size: int = 40,
+        dropout: float = 0.1,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.autoregressive = True
+        
+        self.model = nn.GRU(
+            input_size=self.input_size,
+            hidden_size=f_hidden_size,
+            num_layers=num_layers,
+            dropout=dropout,
+            batch_first=True
+        )
+        self.linear = nn.Linear(f_hidden_size, self.target_dim)
+        self.loss_fn = nn.MSELoss()
+
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'all')
+        outputs, _ = self.model(inputs)
+        outputs = outputs[:, -self.prediction_length-1:-1, ...]
+        outputs = self.linear(outputs)
+        
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        forecasts = []
+        states = self.encode(batch_data)
+        past_target_cdf = batch_data.past_target_cdf
+        
+        for k in range(self.prediction_length):
+            current_batch_data = ProbTSBatchData({
+                'target_dimension_indicator': batch_data.target_dimension_indicator,
+                'past_target_cdf': past_target_cdf,
+                'future_time_feat': batch_data.future_time_feat[:, k : k + 1:, ...]
+            }, device=batch_data.device)
+
+            outputs, states = self.decode(current_batch_data, states)
+            outputs = self.linear(outputs)
+            forecasts.append(outputs)
+
+            past_target_cdf = torch.cat(
+                (past_target_cdf, outputs), dim=1
+            )
+
+        forecasts = torch.cat(forecasts, dim=1).reshape(
+            -1, self.prediction_length, self.target_dim)
+        return forecasts.unsqueeze(1)
+
+    def encode(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        outputs, states = self.model(inputs)
+        return states
+
+    def decode(self, batch_data, states=None):
+        inputs = self.get_inputs(batch_data, 'decode')
+        outputs, states = self.model(inputs, states)
+        return outputs, states
diff --git a/probts/model/forecaster/point_forecaster/itransformer.py b/probts/model/forecaster/point_forecaster/itransformer.py
new file mode 100644
index 0000000..b843d10
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/itransformer.py
@@ -0,0 +1,112 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from iTransformer
+# - Source: https://github.com/thuml/iTransformer
+# - Paper: iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
+# - License: MIT License
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+from probts.model.forecaster import Forecaster
+from probts.model.nn.layers.Transformer_EncDec import Encoder, EncoderLayer
+from probts.model.nn.layers.SelfAttention_Family import FullAttention, AttentionLayer
+from probts.model.nn.layers.Embed import DataEmbedding_inverted
+
+class iTransformer(Forecaster):
+    def __init__(
+        self,
+        factor: int = 1,
+        n_heads: int = 8,
+        activation: str = 'gelu',
+        e_layers: int = 2,
+        output_attention: bool = False,
+        d_ff: int = 512,
+        label_len: int = 48,
+        use_norm: bool = True,
+        class_strategy:str = 'projection',
+        dropout: float = 0.1,
+        f_hidden_size: int = 512,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        
+        self.label_len = label_len
+        
+        self.use_norm = use_norm
+        # Embedding
+        self.enc_embedding = DataEmbedding_inverted(self.context_length, f_hidden_size,
+                                                    dropout)
+        self.class_strategy = class_strategy
+        # Encoder-only architecture
+        self.model_encoder = Encoder(
+            [
+                EncoderLayer(
+                    AttentionLayer(
+                        FullAttention(False, factor, attention_dropout=dropout,
+                                      output_attention=output_attention), f_hidden_size, n_heads),
+                    f_hidden_size,
+                    d_ff,
+                    dropout=dropout,
+                    activation=activation
+                ) for l in range(e_layers)
+            ],
+            norm_layer=torch.nn.LayerNorm(f_hidden_size)
+        )
+        self.projector = nn.Linear(f_hidden_size, self.prediction_length, bias=True)
+        self.loss_fn = nn.MSELoss()
+
+    def forward(self, inputs):
+        if self.use_time_feat:
+            past_target = inputs[:,:,:self.target_dim]
+            x_mark_enc = inputs[:,:,-self.target_dim:]
+        else:
+            past_target = inputs
+            x_mark_enc = None
+            
+        
+        if self.use_norm:
+            # Normalization from Non-stationary Transformer
+            means = past_target.mean(1, keepdim=True).detach()
+            past_target = past_target - means
+            stdev = torch.sqrt(torch.var(past_target, dim=1, keepdim=True, unbiased=False) + 1e-5)
+            past_target /= stdev
+
+        _, _, N = past_target.shape # B L N
+        # B: batch_size;    E: d_model; 
+        # L: seq_len;       S: pred_len;
+        # N: number of variate (tokens), can also includes covariates
+
+        # Embedding
+        # B L N -> B N E                (B L N -> B L E in the vanilla Transformer)
+        enc_out = self.enc_embedding(past_target, x_mark_enc) # covariates (e.g timestamp) can be also embedded as tokens
+        
+        # B N E -> B N E                (B L E -> B L E in the vanilla Transformer)
+        # the dimensions of embedded time series has been inverted, and then processed by native attn, layernorm and ffn modules
+        enc_out, attns = self.model_encoder(enc_out, attn_mask=None)
+
+        # B N E -> B N S -> B S N 
+        dec_out = self.projector(enc_out).permute(0, 2, 1)[:, :, :N] # filter the covariates
+
+        if self.use_norm:
+            # De-Normalization from Non-stationary Transformer
+            dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.prediction_length, 1))
+            dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.prediction_length, 1))
+
+        return dec_out[:, -self.prediction_length:, :]
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        output = self(inputs)
+
+        return output.unsqueeze(1)
+
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        outputs = self(inputs)
+        
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
\ No newline at end of file
diff --git a/probts/model/forecaster/point_forecaster/linear.py b/probts/model/forecaster/point_forecaster/linear.py
new file mode 100644
index 0000000..74c81de
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/linear.py
@@ -0,0 +1,56 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from LTSF-Linear
+# - Source: https://github.com/cure-lab/LTSF-Linear
+# - Paper: Are Transformers Effective for Time Series Forecasting?
+# - License: Apache-2.0
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+
+from probts.model.forecaster import Forecaster
+
+
+class LinearForecaster(Forecaster):
+    def __init__(
+        self,
+        individual: bool = True,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.individual = individual
+        
+        if self.individual:
+            self.linear = nn.ModuleList()
+            for i in range(self.input_size):
+                self.linear.append(nn.Linear(self.context_length, self.prediction_length))
+        else:
+            self.linear = nn.Linear(self.context_length, self.prediction_length)
+        self.out_linear = nn.Linear(self.input_size, self.target_dim)
+        self.loss_fn = nn.MSELoss()
+
+    def forward(self, x):
+        if self.individual:
+            outputs = torch.zeros([x.size(0), self.prediction_length, x.size(2)], dtype=x.dtype).to(x.device)
+            for i in range(self.input_size):
+                outputs[:, :, i] = self.linear[i](x[:, :, i])
+        else:
+            outputs = self.linear(x.permute(0,2,1)).permute(0,2,1)
+        outputs = self.out_linear(outputs)
+        return outputs
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        forecasts = self(inputs).unsqueeze(1)
+        return forecasts
+
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        outputs = self(inputs)
+        
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
diff --git a/probts/model/forecaster/point_forecaster/mean.py b/probts/model/forecaster/point_forecaster/mean.py
new file mode 100644
index 0000000..2529ed8
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/mean.py
@@ -0,0 +1,33 @@
+import torch
+from einops import repeat
+from probts.model.forecaster import Forecaster
+
+
+class MeanForecaster(Forecaster):
+    def __init__(
+        self,
+        global_mean: torch.Tensor,
+        mode: str = 'batch',
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.global_mean = global_mean
+        self.mode = mode
+        self.no_training = True
+
+    @property
+    def name(self):
+        return self.mode + self.__class__.__name__
+        
+    def forecast(self, batch_data, num_samples=None):
+        B = batch_data.past_target_cdf.shape[0]
+        if self.mode == 'global':
+            outputs = self.global_mean.clone()
+        elif self.mode == 'batch':
+            outputs = torch.mean(batch_data.past_target_cdf, dim=1)
+            outputs = torch.mean(outputs, dim=0)
+        else:
+            raise ValueError(f"Unsupported mode: {self.mode}")
+            
+        outputs = repeat(outputs,'d -> b n l d', b=B, n=1, l=self.prediction_length)
+        return outputs
diff --git a/probts/model/forecaster/point_forecaster/nhits.py b/probts/model/forecaster/point_forecaster/nhits.py
new file mode 100644
index 0000000..2bdb2cc
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/nhits.py
@@ -0,0 +1,468 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from NeuralForecast
+# - Source: https://github.com/Nixtla/neuralforecast
+# - Paper: N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
+# - License: Apache-2.0
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import numpy as np
+from einops import rearrange, repeat
+from functools import partial
+from typing import List, Tuple
+
+from probts.model.forecaster import Forecaster
+
+
+class StaticFeaturesEncoder(nn.Module):
+    def __init__(self, in_features, out_features):
+        super().__init__()
+        layers = [nn.Dropout(p=0.5), nn.Linear(in_features=in_features, out_features=out_features), nn.ReLU()]
+        self.encoder = nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        return x
+
+
+class IdentityBasis(nn.Module):
+    def __init__(self, backcast_size: int, forecast_size: int, interpolation_mode: str):
+        super().__init__()
+        assert (interpolation_mode in ["linear", "nearest"]) or ("cubic" in interpolation_mode)
+        self.forecast_size = forecast_size
+        self.backcast_size = backcast_size
+        self.interpolation_mode = interpolation_mode
+
+    def forward(
+        self,
+        backcast_theta: torch.Tensor,
+        forecast_theta: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        backcast = backcast_theta
+        knots = forecast_theta
+
+        if self.interpolation_mode == "nearest":
+            knots = knots[:, None, :]
+            forecast = F.interpolate(knots, size=self.forecast_size, mode=self.interpolation_mode)
+            forecast = forecast[:, 0, :]
+        elif self.interpolation_mode == "linear":
+            knots = knots[:, None, :]
+            forecast = F.interpolate(
+                knots, size=self.forecast_size, mode=self.interpolation_mode
+            )  # , align_corners=True)
+            forecast = forecast[:, 0, :]
+        elif "cubic" in self.interpolation_mode:
+            batch_size = int(self.interpolation_mode.split("-")[-1])
+            knots = knots[:, None, None, :]
+            forecast = torch.zeros((len(knots), self.forecast_size)).to(knots.device)
+            n_batches = int(np.ceil(len(knots) / batch_size))
+            for i in range(n_batches):
+                forecast_i = F.interpolate(
+                    knots[i * batch_size : (i + 1) * batch_size], size=self.forecast_size, mode="bicubic"
+                )  # , align_corners=True)
+                forecast[i * batch_size : (i + 1) * batch_size] += forecast_i[:, 0, 0, :]
+
+        return backcast, forecast
+
+
+def init_weights(module, initialization):
+    if type(module) == torch.nn.Linear:
+        if initialization == "orthogonal":
+            torch.nn.init.orthogonal_(module.weight)
+        elif initialization == "he_uniform":
+            torch.nn.init.kaiming_uniform_(module.weight)
+        elif initialization == "he_normal":
+            torch.nn.init.kaiming_normal_(module.weight)
+        elif initialization == "glorot_uniform":
+            torch.nn.init.xavier_uniform_(module.weight)
+        elif initialization == "glorot_normal":
+            torch.nn.init.xavier_normal_(module.weight)
+        elif initialization == "lecun_normal":
+            pass  # torch.nn.init.normal_(module.weight, 0.0, std=1/np.sqrt(module.weight.numel()))
+        else:
+            assert 1 < 0, f"Initialization {initialization} not found"
+
+
+ACTIVATIONS = ["ReLU", "Softplus", "Tanh", "SELU", "LeakyReLU", "PReLU", "Sigmoid"]
+
+
+class NHiTSBlock(nn.Module):
+    """
+    N-HiTS block which takes a basis function as an argument.
+    """
+
+    def __init__(
+        self,
+        context_length: int,
+        prediction_length: int,
+        output_size: int,
+        covariate_size: int,
+        static_size: int,
+        static_hidden_size: int,
+        n_theta: int,
+        hidden_size: List[int],
+        pooling_sizes: int,
+        pooling_mode: str,
+        basis: nn.Module,
+        n_layers: int,
+        batch_normalization: bool,
+        dropout: float,
+        activation: str,
+    ):
+        super().__init__()
+
+        assert pooling_mode in ["max", "average"]
+
+        self.context_length_pooled = int(np.ceil(context_length / pooling_sizes))
+
+        if static_size == 0:
+            static_hidden_size = 0
+
+        self.context_length = context_length
+        self.output_size = [output_size]
+        self.n_theta = n_theta
+        self.prediction_length = prediction_length
+        self.static_size = static_size
+        self.static_hidden_size = static_hidden_size
+        self.covariate_size = covariate_size
+        self.pooling_sizes = pooling_sizes
+        self.batch_normalization = batch_normalization
+        self.dropout = dropout
+
+        hidden1 = [self.context_length_pooled * len(self.output_size) + (self.context_length + self.prediction_length) * self.covariate_size + self.static_hidden_size]
+        self.hidden_size = hidden1 + hidden_size
+
+
+
+        assert activation in ACTIVATIONS, f"{activation} is not in {ACTIVATIONS}"
+        activ = getattr(nn, activation)()
+
+        if pooling_mode == "max":
+            self.pooling_layer = nn.MaxPool1d(kernel_size=self.pooling_sizes, stride=self.pooling_sizes, ceil_mode=True)
+        elif pooling_mode == "average":
+            self.pooling_layer = nn.AvgPool1d(kernel_size=self.pooling_sizes, stride=self.pooling_sizes, ceil_mode=True)
+
+        hidden_layers = []
+        for i in range(n_layers):
+            hidden_layers.append(nn.Linear(in_features=self.hidden_size[i], out_features=self.hidden_size[i + 1]))
+            hidden_layers.append(activ)
+
+            if self.batch_normalization:
+                hidden_layers.append(nn.BatchNorm1d(num_features=self.hidden_size[i + 1]))
+
+            if self.dropout > 0:
+                hidden_layers.append(nn.Dropout(p=self.dropout))
+
+        output_layer = [
+            nn.Linear(
+                in_features=self.hidden_size[-1],
+                out_features=context_length * len(self.output_size) + n_theta * sum(self.output_size),
+            )
+        ]
+        layers = hidden_layers + output_layer
+
+        # static_size is computed with data, static_hidden_size is provided by user, if 0 no statics are used
+        if (self.static_size > 0) and (self.static_hidden_size > 0):
+            self.static_encoder = StaticFeaturesEncoder(in_features=static_size, out_features=static_hidden_size)
+        self.layers = nn.Sequential(*layers)
+        self.basis = basis
+
+    def forward(
+        self, encoder_y: torch.Tensor, encoder_x_t: torch.Tensor, decoder_x_t: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        batch_size = len(encoder_y)
+
+        encoder_y = encoder_y.transpose(1, 2)
+        # Pooling layer to downsample input
+        encoder_y = self.pooling_layer(encoder_y)
+
+        encoder_y = encoder_y.transpose(1, 2).reshape(batch_size, -1)
+
+
+        if self.covariate_size > 0:
+            encoder_y = torch.cat(
+                (
+                    encoder_y,
+                    encoder_x_t.reshape(batch_size, -1),
+                    decoder_x_t.reshape(batch_size, -1),
+                ),
+                1,
+            )
+
+        # Compute local projection weights and projection
+        theta = self.layers(encoder_y)
+        backcast_theta = theta[:, : self.context_length * len(self.output_size)].reshape(-1, self.context_length)
+        forecast_theta = theta[:, self.context_length * len(self.output_size) :].reshape(-1, self.n_theta)
+        backcast, forecast = self.basis(backcast_theta, forecast_theta)
+        backcast = backcast.reshape(-1, len(self.output_size), self.context_length).transpose(1, 2)
+        forecast = forecast.reshape(-1, sum(self.output_size), self.prediction_length).transpose(1, 2)
+
+        return backcast, forecast
+
+
+
+class NHiTS(Forecaster):
+    def __init__(
+        self,
+        n_blocks: list,
+        pooling_mode,
+        interpolation_mode,
+        dropout,
+        activation,
+        initialization,
+        batch_normalization,
+        shared_weights,
+        output_size: int = 1,
+        hidden_size: int = 512,
+        naive_level: bool = True,
+        static_size: int = 0,
+        static_hidden_size: int = 0,
+        n_layers: int = 2,
+        pooling_sizes: list = None,
+        downsample_frequencies: list = None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        """
+        N-HiTS model.
+
+        Parameters
+        ----------
+        n_time_in: int
+            Multiplier to get insample size.
+            Insample size = n_time_in * output_size
+        n_time_out: int
+            Forecast horizon.
+        shared_weights: bool
+            If True, repeats first block.
+        activation: str
+            Activation function.
+            An item from ['relu', 'softplus', 'tanh', 'selu', 'lrelu', 'prelu', 'sigmoid'].
+        initialization: str
+            Initialization function.
+            An item from ['orthogonal', 'he_uniform', 'glorot_uniform', 'glorot_normal', 'lecun_normal'].
+        stack_types: List[str]
+            List of stack types.
+            Subset from ['identity'].
+        n_blocks: List[int]
+            Number of blocks for each stack type.
+            Note that len(n_blocks) = len(stack_types).
+        n_layers: List[int]
+            Number of layers for each stack type.
+            Note that len(n_layers) = len(stack_types).
+        n_theta_hidden: List[List[int]]
+            Structure of hidden layers for each stack type.
+            Each internal list should contain the number of units of each hidden layer.
+            Note that len(n_theta_hidden) = len(stack_types).
+        n_pool_kernel_size List[int]:
+            Pooling size for input for each stack.
+            Note that len(n_pool_kernel_size) = len(stack_types).
+        n_freq_downsample List[int]:
+            Downsample multiplier of output for each stack.
+            Note that len(n_freq_downsample) = len(stack_types).
+        batch_normalization: bool
+            Whether perform batch normalization.
+        dropout_prob_theta: float
+            Float between (0, 1).
+            Dropout for Nbeats basis.
+        """
+
+        n_stacks = len(n_blocks)
+        covariate_size = 0
+        if self.use_feat_idx_emb:
+            covariate_size = covariate_size + self.feat_idx_emb_dim
+        if self.use_time_feat:
+            covariate_size = covariate_size + self.time_feat_dim
+        self.covariate_size = covariate_size
+        self.output_size = output_size
+        self.naive_level = naive_level
+
+        n_layers = [n_layers] * n_stacks
+        hidden_size = n_stacks * [2 * [hidden_size]]
+
+        if pooling_sizes is None:
+            pooling_sizes = np.exp2(np.round(np.linspace(0.49, np.log2(self.prediction_length / 2), n_stacks)))
+            pooling_sizes = [int(x) for x in pooling_sizes[::-1]]
+
+        if downsample_frequencies is None:
+            downsample_frequencies = [min(self.prediction_length, int(np.power(x, 1.5))) for x in pooling_sizes]
+
+        blocks = self.create_stack(
+            n_blocks=n_blocks,
+            context_length=self.context_length,
+            prediction_length=self.prediction_length,
+            output_size=output_size,
+            covariate_size=covariate_size,
+            static_size=static_size,
+            static_hidden_size=static_hidden_size,
+            n_layers=n_layers,
+            hidden_size=hidden_size,
+            pooling_sizes=pooling_sizes,
+            downsample_frequencies=downsample_frequencies,
+            pooling_mode=pooling_mode,
+            interpolation_mode=interpolation_mode,
+            batch_normalization=batch_normalization,
+            dropout=dropout,
+            activation=activation,
+            shared_weights=shared_weights,
+            initialization=initialization,
+        )
+        self.blocks = torch.nn.ModuleList(blocks)
+        self.loss_fn = nn.MSELoss()
+
+    def create_stack(
+        self,
+        n_blocks,
+        context_length,
+        prediction_length,
+        output_size,
+        covariate_size,
+        static_size,
+        static_hidden_size,
+        n_layers,
+        hidden_size,
+        pooling_sizes,
+        downsample_frequencies,
+        pooling_mode,
+        interpolation_mode,
+        batch_normalization,
+        dropout,
+        activation,
+        shared_weights,
+        initialization,
+    ):
+        block_list = []
+
+        for i in range(len(n_blocks)):
+            for block_id in range(n_blocks[i]):
+                # Batch norm only on first block
+                if (len(block_list) == 0) and (batch_normalization):
+                    batch_normalization_block = True
+                else:
+                    batch_normalization_block = False
+
+                # Shared weights
+                if shared_weights and block_id > 0:
+                    nbeats_block = block_list[-1]
+                else:
+                    n_theta = max(prediction_length // downsample_frequencies[i], 1)
+                    basis = IdentityBasis(
+                        backcast_size=context_length,
+                        forecast_size=prediction_length,
+                        interpolation_mode=interpolation_mode,
+                    )
+
+                    nbeats_block = NHiTSBlock(
+                        context_length=context_length,
+                        prediction_length=prediction_length,
+                        output_size=output_size,
+                        covariate_size=covariate_size,
+                        static_size=static_size,
+                        static_hidden_size=static_hidden_size,
+                        n_theta=n_theta,
+                        hidden_size=hidden_size[i],
+                        pooling_sizes=pooling_sizes[i],
+                        pooling_mode=pooling_mode,
+                        basis=basis,
+                        n_layers=n_layers[i],
+                        batch_normalization=batch_normalization_block,
+                        dropout=dropout,
+                        activation=activation,
+                    )
+
+                # Select type of evaluation and apply it to all layers of block
+                init_function = partial(init_weights, initialization=initialization)
+                nbeats_block.layers.apply(init_function)
+                block_list.append(nbeats_block)
+        return block_list
+
+        
+
+    def encoder(self, encoder_y, encoder_x_t, decoder_x_t):
+        # encoder_y: [B L D]
+        residuals = (encoder_y)
+        level = encoder_y[:, -1:].repeat(1, self.prediction_length, 1)  # Level with Naive1
+        forecast_level = level.repeat_interleave(torch.tensor(self.output_size, device=level.device), dim=2)
+
+        # level with last available observation
+        if self.naive_level:
+            block_forecasts = [forecast_level]
+            forecast = block_forecasts[0]
+        else:
+            block_forecasts = []
+            forecast = torch.zeros_like(forecast_level, device=forecast_level.device)
+
+        # forecast by block
+        for block in self.blocks:
+            block_backcast, block_forecast = block(
+                encoder_y=residuals, encoder_x_t=encoder_x_t, decoder_x_t=decoder_x_t
+            )
+            residuals = (residuals - block_backcast) # * encoder_mask
+
+            forecast = forecast + block_forecast
+        return forecast
+
+    def get_cov(self, inputs):
+        if self.use_feat_idx_emb:
+            if self.use_time_feat:
+                encoder_dim_fea = inputs[:, : self.context_length, self.target_dim:-self.time_feat_dim]  # [B L K*D]
+                decoder_dim_fea = inputs[:, -self.prediction_length:, self.target_dim:-self.time_feat_dim]  # [B L K*D]
+            else:
+                encoder_dim_fea = inputs[:, : self.context_length, self.target_dim:]  # [B L K*D]
+                decoder_dim_fea = inputs[:, -self.prediction_length:, self.target_dim:]  # [B L K*D]
+
+            encoder_dim_fea = rearrange(encoder_dim_fea, "b l (k d) -> (b k) l d", k=self.target_dim, d=self.feat_idx_emb_dim)
+            decoder_dim_fea = rearrange(decoder_dim_fea, "b l (k d) -> (b k) l d", k=self.target_dim, d=self.feat_idx_emb_dim)
+        else:
+            encoder_dim_fea = []
+
+        if self.time_feat_dim:
+            encoder_time_fea = inputs[:, : self.context_length, -self.time_feat_dim: ] # [B L Dt]
+            encoder_time_fea = repeat(encoder_time_fea, 'b l d -> (b k) l d', k=self.target_dim)
+
+            decoder_time_fea = inputs[:, -self.prediction_length:, -self.time_feat_dim: ] # [B L Dt]
+            decoder_time_fea = repeat(decoder_time_fea, 'b l d -> (b k) l d', k=self.target_dim)
+
+        else:
+            encoder_time_fea = []
+
+        if self.use_feat_idx_emb and self.use_time_feat:
+            encoder_x_t = torch.cat([encoder_dim_fea, encoder_time_fea], dim=-1)
+            decoder_x_t = torch.cat([decoder_dim_fea, decoder_time_fea], dim=-1)
+        elif self.use_feat_idx_emb:
+            encoder_x_t, decoder_x_t = encoder_dim_fea, decoder_dim_fea
+        elif self.use_time_feat:
+            encoder_x_t, decoder_x_t = encoder_time_fea, decoder_time_fea
+        else:
+            encoder_x_t, decoder_x_t = None, None
+        return encoder_x_t, decoder_x_t
+
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'all') # [B L D]
+        
+        # Encode
+        encoder_y = inputs[:, : self.context_length, :self.target_dim] # [B L K]
+        encoder_y = rearrange(encoder_y, "b l k -> (b k) l 1")
+        encoder_x_t, decoder_x_t = self.get_cov(inputs)
+        outputs = self.encoder(encoder_y, encoder_x_t, decoder_x_t)
+        outputs = rearrange(outputs, "(b k) l 1 -> b l k", k=self.target_dim)
+        
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'all') # [B L D]
+        encoder_y = inputs[:, : self.context_length, :self.target_dim] # [B L K]
+        encoder_y = rearrange(encoder_y, "b l k -> (b k) l 1")
+        encoder_x_t, decoder_x_t = self.get_cov(inputs)
+        output = self.encoder(encoder_y,encoder_x_t, decoder_x_t)
+        outputs = rearrange(output, "(b k) l 1 -> b l k", k=self.target_dim)
+        return outputs.unsqueeze(1)
diff --git a/probts/model/forecaster/point_forecaster/nlinear.py b/probts/model/forecaster/point_forecaster/nlinear.py
new file mode 100644
index 0000000..3bae523
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/nlinear.py
@@ -0,0 +1,66 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from LTSF-Linear
+# - Source: https://github.com/cure-lab/LTSF-Linear
+# - Paper: Are Transformers Effective for Time Series Forecasting?
+# - License: Apache-2.0
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+from probts.model.forecaster import Forecaster
+
+
+class NLinear(Forecaster):
+    def __init__(
+        self,
+        individual: bool,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        if self.input_size != self.target_dim:
+            self.enc_linear = nn.Linear(
+                in_features=self.input_size, out_features=self.target_dim
+            )
+        else:
+            self.enc_linear = nn.Identity()
+
+        self.target_dim = self.target_dim
+        self.individual = individual
+        if individual:
+            self.Linear = nn.ModuleList()
+            for i in range(self.target_dim):
+                self.Linear.append(nn.Linear(self.context_length,self.prediction_length))
+        else:
+            self.Linear = nn.Linear(self.context_length, self.prediction_length)
+        self.loss_fn = nn.MSELoss()
+
+    def forward(self, inputs):
+        seq_last = inputs[:,-1:,:].detach()
+        inputs = inputs - seq_last
+        if self.individual:
+            output = torch.zeros([inputs.size(0),self.prediction_length,inputs.size(2)],dtype=inputs.dtype).to(inputs.device)
+            for i in range(self.target_dim):
+                output[:,:,i] = self.Linear[i](inputs[:,:,i])
+        else:
+            output = self.Linear(inputs.permute(0,2,1)).permute(0,2,1)
+        output = output + seq_last
+        return output
+
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'all')
+        inputs = inputs[:, : self.context_length, ...]
+        inputs = self.enc_linear(inputs)
+        outputs = self(inputs)
+        
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = self.enc_linear(inputs)
+        outputs = self(inputs)
+        return outputs.unsqueeze(1)
diff --git a/probts/model/forecaster/point_forecaster/patchtst.py b/probts/model/forecaster/point_forecaster/patchtst.py
new file mode 100644
index 0000000..c1f1a26
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/patchtst.py
@@ -0,0 +1,127 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PatchTST
+# - Source: https://github.com/yuqinie98/PatchTST/tree/main
+# - Paper: PatchTST: A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
+# - License: Apache-2.0
+
+# We thank the authors for their contributions.
+# -----
+# ----------------------------------------------------------------------------
+
+
+import torch.nn as nn
+from torch import Tensor
+from typing import Optional
+
+from probts.model.forecaster import Forecaster
+from probts.model.nn.layers.PatchTST_backbone import PatchTST_backbone
+from probts.model.nn.layers.PatchTST_layers import series_decomp
+
+class PatchTST(Forecaster):
+    def __init__(
+        self,
+        stride: int,
+        patch_len: int,
+        padding_patch: str = None,
+        max_seq_len: int = 1024,
+        n_layers:int = 3,
+        n_heads = 16,
+        d_k: int = None,
+        d_v: int = None,
+        d_ff: int = 256,
+        attn_dropout: float = 0.,
+        dropout: float = 0.,
+        act: str = "gelu", 
+        res_attention: bool = True,
+        pre_norm: bool = False,
+        store_attn: bool = False,
+        pe: str = 'zeros',
+        learn_pe: bool = True,
+        attn_mask: Optional[Tensor] = None,
+        individual: bool = False,
+        head_type: str = 'flatten',
+        padding_var: Optional[int] = None, 
+        revin: bool = True,
+        key_padding_mask: str = 'auto',
+        affine: bool = False,
+        subtract_last: bool = False,
+        decomposition: bool = False,
+        kernel_size: int = 3,
+        fc_dropout: float = 0.,
+        head_dropout: float = 0.,
+        f_hidden_size: int = 40,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        
+        if self.input_size != self.target_dim:
+            self.enc_linear = nn.Linear(
+                in_features=self.input_size, out_features=self.target_dim
+            )
+        else:
+            self.enc_linear = nn.Identity()
+
+        # Load parameters
+        c_in = self.input_size
+        context_window = self.context_length
+        target_window = self.prediction_length
+
+        # Model
+        self.decomposition = decomposition
+        if self.decomposition:
+            self.decomp_module = series_decomp(kernel_size)
+            self.model_trend = PatchTST_backbone(c_in=c_in, context_window=context_window, target_window=target_window, patch_len=patch_len, stride=stride, 
+                                  max_seq_len=max_seq_len, n_layers=n_layers, d_model=f_hidden_size,
+                                  n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, attn_dropout=attn_dropout,
+                                  dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, 
+                                  attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
+                                  pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch,
+                                  pretrain_head=False, head_type=head_type, individual=individual, revin=revin, affine=affine,
+                                  subtract_last=subtract_last)
+            self.model_res = PatchTST_backbone(c_in=c_in, context_window=context_window, target_window=target_window, patch_len=patch_len, stride=stride, 
+                                  max_seq_len=max_seq_len, n_layers=n_layers, d_model=f_hidden_size,
+                                  n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, attn_dropout=attn_dropout,
+                                  dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, 
+                                  attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
+                                  pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch,
+                                  pretrain_head=False, head_type=head_type, individual=individual, revin=revin, affine=affine,
+                                  subtract_last=subtract_last)
+        else:
+            self.model = PatchTST_backbone(c_in=c_in, context_window=context_window, target_window=target_window, patch_len=patch_len, stride=stride, 
+                                  max_seq_len=max_seq_len, n_layers=n_layers, d_model=f_hidden_size,
+                                  n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, attn_dropout=attn_dropout,
+                                  dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, 
+                                  attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
+                                  pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch,
+                                  pretrain_head=False, head_type=head_type, individual=individual, revin=revin, affine=affine,
+                                  subtract_last=subtract_last)
+        self.loss_fn = nn.MSELoss()
+    
+    def forward(self, x):
+        if self.decomposition:
+            res_init, trend_init = self.decomp_module(x)
+            res_init, trend_init = res_init.permute(0,2,1), trend_init.permute(0,2,1)  # x: [Batch, Channel, Input length]
+            res = self.model_res(res_init)
+            trend = self.model_trend(trend_init)
+            x = res + trend
+            x = x.permute(0,2,1)    # x: [Batch, Input length, Channel]
+        else:
+            x = x.permute(0,2,1)    # x: [Batch, Channel, Input length]
+            x = self.model(x)
+            x = x.permute(0,2,1)    # x: [Batch, Input length, Channel]
+        return x
+
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = self.enc_linear(inputs)
+        outputs = self(inputs)
+        
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = self.enc_linear(inputs)
+        outputs = self(inputs)
+        return outputs.unsqueeze(1)
diff --git a/probts/model/forecaster/point_forecaster/timer.py b/probts/model/forecaster/point_forecaster/timer.py
new file mode 100644
index 0000000..b4a2ba3
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/timer.py
@@ -0,0 +1,93 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from Large-Time-Series-Model
+# - Source: https://github.com/thuml/Large-Time-Series-Model
+# - Paper: Timer: Generative Pre-trained Transformers Are Large Time Series Models
+# - License: MIT License
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+from einops import rearrange, repeat
+from torch import nn
+
+from probts.model.forecaster import Forecaster
+
+
+class Model(nn.Module):
+    """
+    Paper link: https://arxiv.org/pdf/2402.02368.pdf
+    """
+
+    def __init__(self, ckpt_path):
+        super().__init__()
+        if ckpt_path and ckpt_path != "":
+            if ckpt_path.endswith('.pt'):
+                # print(f"Loading Timer model from {ckpt_path}")
+                self.timer = torch.jit.load(ckpt_path)
+        else:
+            raise NotImplementedError
+
+    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
+        return self.timer(x_enc, x_mark_enc, x_dec, x_mark_dec)
+
+
+class Timer(Forecaster):
+    def __init__(
+        self,
+        label_len: int = 576,
+        ckpt_path: str = None,
+        ckpt_path_finetune: str = None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.no_training = True
+        
+        self.output_patch_len = 96 # fixed by the pre-trained model
+        self.label_len = label_len
+
+        # Load Timer
+        self.model = Model(ckpt_path)
+        if ckpt_path_finetune:
+            print(f"Loading Timer finetune model from {ckpt_path_finetune}")
+            self.model.load_state_dict(torch.load(ckpt_path_finetune))
+       
+
+    def forecast(self, batch_data, num_samples=None):        
+        # for now, we only support batch_size=1
+        B, _, K = batch_data.past_target_cdf.shape
+        inputs = batch_data.past_target_cdf[:, -self.context_length:, ...]
+        x_mark_enc = batch_data.past_time_feat[:, -self.context_length:, ...]
+        x_mark_dec = batch_data.future_time_feat
+        x_mark_dec = torch.cat([x_mark_enc[:, -self.label_len:, :], x_mark_dec], dim=1)
+
+        inputs = rearrange(inputs, 'b l k -> (b k) l 1')
+        x_mark_enc = repeat(x_mark_enc, 'b l f -> (b k) l f', k=K)
+        x_mark_dec = repeat(x_mark_dec, 'b l f -> (b k) l f', k=K)
+
+        dec_inp = torch.zeros_like(inputs[:, -self.prediction_length:, :]).float()
+        dec_inp = torch.cat((inputs[:, -self.label_len:, ...], dec_inp), dim=1).float()
+
+        inference_steps = self.prediction_length // self.output_patch_len
+        dis = self.prediction_length - inference_steps * self.output_patch_len
+        if dis != 0:
+            inference_steps += 1
+
+        pred_y = []
+
+        for j in range(inference_steps):
+            if len(pred_y) != 0:
+                inputs = torch.cat([inputs[:, self.output_patch_len:, :], pred_y[-1]], dim=1)
+                tmp = x_mark_dec[:, j - 1:j, :]
+                x_mark_enc = torch.cat([x_mark_enc[:, 1:, :], tmp], dim=1)
+
+            outputs = self.model(inputs, x_mark_enc, dec_inp, x_mark_dec)
+            pred_y.append(outputs[:, -self.output_patch_len:, :])
+
+        pred_y = torch.cat(pred_y, dim=1)
+        if dis != 0:
+            pred_y = pred_y[:, :-dis, :]
+        pred_y = rearrange(pred_y, '(b k) l 1 -> b l k', b=B, k=K)
+        pred_y = pred_y[:, :self.prediction_length, :]
+        return pred_y.unsqueeze(1)
diff --git a/probts/model/forecaster/point_forecaster/timesfm.py b/probts/model/forecaster/point_forecaster/timesfm.py
new file mode 100644
index 0000000..9cbf265
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/timesfm.py
@@ -0,0 +1,85 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from timesfm
+# - Source: https://github.com/google-research/timesfm
+# - Paper: A decoder-only foundation model for time-series forecasting
+# - License: Apache License 2.0
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import numpy as np
+import torch
+from einops import rearrange
+
+from probts.model.forecaster import Forecaster
+
+from submodules.timesfm.src.timesfm import TimesFm
+
+class TimesFM(Forecaster):
+    def __init__(
+        self,
+        input_patch_len: int = 32,
+        output_patch_len: int = 128,
+        num_layers: int = 20,
+        model_dims: int = 1280,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.no_training = True
+        
+        if (type(self.target_dim).__name__=='dict'):
+            for dataset_name in self.target_dim:
+                target_dim = target_dim[dataset_name]
+                freq = freq[dataset_name]
+        else:
+            freq = self.freq
+                
+        if (type(self.context_length).__name__=='list'):
+            context_length = context_length[0]
+            
+        if (type(self.prediction_length).__name__=='list'):
+            # prediction_length = prediction_length[0]
+            prediction_length = max(prediction_length)
+        
+        self.tfm = TimesFm(
+            context_len=self.context_length,
+            horizon_len=self.prediction_length,
+            input_patch_len=input_patch_len,
+            output_patch_len=output_patch_len,
+            num_layers=num_layers,
+            model_dims=model_dims,
+            backend='cpu',
+        )
+        self.tfm.load_from_checkpoint(repo_id="google/timesfm-1.0-200m")
+        
+        freq_dict = {'h': 0, 'min': 0, 'd': 0, 'b': 0, 'u': 0, 'w': 1, 'm': 1, 'q': 2, 'y': 2}
+        freq = freq.lower()
+        
+        if freq in freq_dict:
+            self.freq = freq_dict[freq]
+        else:
+            self.freq = 0
+
+        print(f"TimesFM - frequency: {freq}, freq_num: {self.freq}")
+    
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = inputs[:, -self.context_length:].cpu()
+        B, _, K = inputs.shape
+        # past_target = batch_data.past_target_cdf[:, -self.context_length:]
+        
+        inputs = np.array(rearrange(inputs, 'b l k -> (b k) l'))
+        frequency_input = [self.freq] * inputs.shape[0]
+        
+        _, out = self.tfm.forecast(
+            inputs,
+            freq=frequency_input,
+        )
+        point_forecast = out[:, :, 5]
+        point_forecast = rearrange(point_forecast, '(b k) l -> b l k', b=B,k=K)
+        
+        point_forecast = torch.tensor(point_forecast[:, :self.prediction_length])
+        return point_forecast.unsqueeze(1)
+    
\ No newline at end of file
diff --git a/probts/model/forecaster/point_forecaster/timesnet.py b/probts/model/forecaster/point_forecaster/timesnet.py
new file mode 100644
index 0000000..ee8e51d
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/timesnet.py
@@ -0,0 +1,163 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from TSLib
+# - Source: https://github.com/libts/tslib
+# - Paper: TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
+# - License:  LGPL-2.1
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.fft
+from probts.model.forecaster import Forecaster
+from probts.model.nn.layers.Embed import DataEmbedding
+from probts.model.nn.layers.Conv_Blocks import Inception_Block_V1
+
+
+def FFT_for_Period(x, k=2):
+    # [B, T, C]
+    xf = torch.fft.rfft(x, dim=1)
+    # find period by amplitudes
+    frequency_list = abs(xf).mean(0).mean(-1)
+    frequency_list[0] = 0
+    _, top_list = torch.topk(frequency_list, k)
+    top_list = top_list.detach().cpu().numpy()
+    period = x.shape[1] // top_list
+    return period, abs(xf).mean(-1)[:, top_list]
+
+
+class TimesBlock(nn.Module):
+    def __init__(self, context_length, prediction_length, top_k, d_model, d_ff, num_kernels):
+        super(TimesBlock, self).__init__()
+        self.seq_len = context_length
+        self.pred_len = prediction_length
+        self.k = top_k
+        # parameter-efficient design
+        self.conv = nn.Sequential(
+            Inception_Block_V1(d_model, d_ff,
+                               num_kernels=num_kernels),
+            nn.GELU(),
+            Inception_Block_V1(d_ff, d_model,
+                               num_kernels=num_kernels)
+        )
+
+    def forward(self, x):
+        B, T, N = x.size()
+        period_list, period_weight = FFT_for_Period(x, self.k)
+
+        res = []
+        for i in range(self.k):
+            period = period_list[i]
+            # padding
+            if (self.seq_len + self.pred_len) % period != 0:
+                length = (
+                                 ((self.seq_len + self.pred_len) // period) + 1) * period
+                padding = torch.zeros([x.shape[0], (length - (self.seq_len + self.pred_len)), x.shape[2]]).to(x.device)
+                out = torch.cat([x, padding], dim=1)
+            else:
+                length = (self.seq_len + self.pred_len)
+                out = x
+            # reshape
+            out = out.reshape(B, length // period, period,
+                              N).permute(0, 3, 1, 2).contiguous()
+            # 2D conv: from 1d Variation to 2d Variation
+            out = self.conv(out)
+            # reshape back
+            out = out.permute(0, 2, 3, 1).reshape(B, -1, N)
+            res.append(out[:, :(self.seq_len + self.pred_len), :])
+        res = torch.stack(res, dim=-1)
+        # adaptive aggregation
+        period_weight = F.softmax(period_weight, dim=1)
+        period_weight = period_weight.unsqueeze(
+            1).unsqueeze(1).repeat(1, T, N, 1)
+        res = torch.sum(res * period_weight, -1)
+        # residual connection
+        res = res + x
+        return res
+
+
+class TimesNet(Forecaster):
+    def __init__(
+        self,
+        n_layers: int = 2,
+        num_kernels: int = 6,
+        top_k: int = 5,
+        d_ff: int = 32,
+        embed: str = 'timeF',
+        dropout: float = 0.1,
+        f_hidden_size: int = 40,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        self.seq_len = self.context_length
+        self.pred_len = self.prediction_length
+
+        self.model = nn.ModuleList(
+            [TimesBlock(self.context_length, self.prediction_length, top_k, f_hidden_size, d_ff, num_kernels)
+                for _ in range(n_layers)]
+        )
+        self.enc_embedding = DataEmbedding(self.target_dim, f_hidden_size, embed, self.freq.lower(), dropout)
+        self.layer = n_layers
+        self.layer_norm = nn.LayerNorm(f_hidden_size)
+
+        self.predict_linear = nn.Linear(
+            self.seq_len, self.pred_len + self.seq_len)
+        self.projection = nn.Linear(
+            f_hidden_size, self.target_dim, bias=True)
+        
+        if self.input_size != self.target_dim:
+            self.enc_linear = nn.Linear(
+                in_features=self.input_size, out_features=self.target_dim
+            )
+        else:
+            self.enc_linear = nn.Identity()
+        self.loss_fn = nn.MSELoss()
+
+    def forward(self, x_enc, x_mark_enc=None):
+        # Normalization from Non-stationary Transformer
+        means = x_enc.mean(1, keepdim=True).detach()
+        x_enc = x_enc - means
+        stdev = torch.sqrt(
+            torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
+        x_enc = x_enc / stdev
+
+        # embedding
+        enc_out = self.enc_embedding(x_enc, x_mark_enc)  # [B,T,C]
+        enc_out = self.predict_linear(enc_out.permute(0, 2, 1)).permute(
+            0, 2, 1)  # align temporal dimension
+        # TimesNet
+        for i in range(self.layer):
+            enc_out = self.layer_norm(self.model[i](enc_out))
+        # porject back
+        dec_out = self.projection(enc_out)
+
+        # De-Normalization from Non-stationary Transformer
+        dec_out = dec_out * \
+                  (stdev[:, 0, :].unsqueeze(1).repeat(
+                      1, self.pred_len + self.seq_len, 1))
+        dec_out = dec_out + \
+                  (means[:, 0, :].unsqueeze(1).repeat(
+                      1, self.pred_len + self.seq_len, 1))
+        return dec_out[:, -self.pred_len:, :]  # [B, L, D]
+
+
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'all')
+        inputs = inputs[:, : self.context_length, ...]
+        inputs = self.enc_linear(inputs)
+        # x: [Batch, Input length, Channel]
+        outputs = self(inputs)
+    
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = self.enc_linear(inputs)
+        outputs = self(inputs)
+        return outputs.unsqueeze(1)
\ No newline at end of file
diff --git a/probts/model/forecaster/point_forecaster/tinytimemixer.py b/probts/model/forecaster/point_forecaster/tinytimemixer.py
new file mode 100644
index 0000000..a3dc47e
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/tinytimemixer.py
@@ -0,0 +1,48 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from granite-tsfm
+# - Source: https://github.com/ibm-granite/granite-tsfm
+# - Paper: Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
+# - License: Apache License 2.0
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+from probts.model.forecaster import Forecaster
+
+from submodules.tsfm.tsfm_public.models.tinytimemixer import TinyTimeMixerForPrediction
+
+
+class TinyTimeMixer(Forecaster):
+    """
+    TinyTimeMixer from https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb
+    prediction length originally 96
+    context length originally 512
+    changes might cause degradation in performance
+    """
+
+    def __init__(
+        self,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.no_training = True
+
+        # TTM model branch
+        # Use main for 512-96 model
+        # Use "1024_96_v1" for 1024-96 model
+        TTM_MODEL_REVISION = "main"
+
+        self.zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(
+            "ibm/TTM", revision=TTM_MODEL_REVISION
+        )
+        
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = inputs[:, -self.context_length:]
+        B, _, K = inputs.shape 
+        # past_target = batch_data.past_target_cdf[:, -self.context_length:]
+        self.zeroshot_model.eval()
+        point_forecast = self.zeroshot_model.forward(inputs).prediction_outputs
+        return point_forecast.unsqueeze(1)
diff --git a/probts/model/forecaster/point_forecaster/transformer.py b/probts/model/forecaster/point_forecaster/transformer.py
new file mode 100644
index 0000000..42dc739
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/transformer.py
@@ -0,0 +1,98 @@
+import torch
+import torch.nn as nn
+
+from probts.data import ProbTSBatchData
+from probts.utils import repeat
+from probts.model.forecaster import Forecaster
+
+
+class TransformerForecaster(Forecaster):
+    def __init__(
+        self,
+        f_hidden_size: int = 32,
+        num_heads: int = 8,
+        num_encoder_layers: int = 3,
+        num_decoder_layers: int = 3,
+        dim_feedforward_scale: int = 4,
+        dropout: float = 0.1,
+        activation: str = 'gelu',
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.autoregressive = True
+        self.f_hidden_size = f_hidden_size
+
+        self.enc_linear = nn.Linear(self.input_size, self.f_hidden_size)
+        self.dec_linear = nn.Linear(self.input_size, self.f_hidden_size)
+        self.model = nn.Transformer(
+            d_model=self.f_hidden_size,
+            nhead=num_heads,
+            num_encoder_layers=num_encoder_layers,
+            num_decoder_layers=num_decoder_layers,
+            dim_feedforward=dim_feedforward_scale * self.f_hidden_size,
+            dropout=dropout,
+            activation=activation
+        )
+
+        self.register_buffer(
+            "tgt_mask",
+            self.model.generate_square_subsequent_mask(self.prediction_length),
+        )
+        self.linear = nn.Linear(self.f_hidden_size, self.target_dim)
+        self.loss_fn = nn.MSELoss()
+
+    def loss(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'all') # [B L D]
+
+        # Encode
+        enc_inputs = inputs[:, :self.context_length, ...]
+        enc_inputs = self.enc_linear(enc_inputs).permute(1, 0, 2)
+        enc_outputs = self.model.encoder(enc_inputs) # [L_in B H]
+
+        # Decode
+        dec_inputs = inputs[:, -self.prediction_length-1:-1, ...]
+        dec_inputs = self.dec_linear(dec_inputs).permute(1, 0, 2)
+        dec_outputs = self.model.decoder(
+            dec_inputs, enc_outputs, tgt_mask=self.tgt_mask)
+        dec_outputs = dec_outputs.permute(1, 0, 2)  # [L_out B D]
+        outputs = self.linear(dec_outputs)
+        
+        loss = self.loss_fn(batch_data.future_target_cdf, outputs)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        forecasts = []
+        states = self.encode(batch_data)
+        past_target_cdf = batch_data.past_target_cdf
+        
+        for k in range(self.prediction_length):
+            current_batch_data = ProbTSBatchData({
+                'target_dimension_indicator': batch_data.target_dimension_indicator,
+                'past_target_cdf': past_target_cdf,
+                'future_time_feat': batch_data.future_time_feat[:, k : k + 1:, ...]
+            }, device=batch_data.device)
+
+            outputs, states = self.decode(current_batch_data, states)
+            outputs = self.linear(outputs)
+            forecasts.append(outputs)
+
+            past_target_cdf = torch.cat(
+                (past_target_cdf, outputs), dim=1
+            )
+
+        forecasts = torch.cat(forecasts, dim=1).reshape(
+            -1, self.prediction_length, self.target_dim)
+        return forecasts.unsqueeze(1)
+
+    def encode(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = self.enc_linear(inputs).permute(1, 0, 2)
+        states = self.model.encoder(inputs)
+        return states
+
+    def decode(self, batch_data, states=None):
+        inputs = self.get_inputs(batch_data, 'decode')
+        inputs = self.dec_linear(inputs).permute(1, 0, 2)
+        outputs = self.model.decoder(inputs, states, tgt_mask=None)
+        return outputs.permute(1, 0, 2), states
diff --git a/probts/model/forecaster/point_forecaster/units.py b/probts/model/forecaster/point_forecaster/units.py
new file mode 100644
index 0000000..d311719
--- /dev/null
+++ b/probts/model/forecaster/point_forecaster/units.py
@@ -0,0 +1,1062 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from UniTS
+# - Source: https://github.com/mims-harvard/UniTS
+# - Paper: UNITS: A Unified Multi-Task Time Series Model
+# - License: MIT License
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import math
+
+import torch
+import torch.nn.functional as F
+from timm.layers import DropPath, Mlp
+from timm.layers.helpers import to_2tuple
+from torch import nn
+
+from probts.model.forecaster import Forecaster
+
+
+def calculate_unfold_output_length(input_length, size, step):
+    # Calculate the number of windows
+    num_windows = (input_length - size) // step + 1
+    return num_windows
+
+
+class CrossAttention(nn.Module):
+    def __init__(
+            self,
+            dim,
+            num_heads=8,
+            qkv_bias=False,
+            qk_norm=False,
+            attn_drop=0.,
+            proj_drop=0.,
+            norm_layer=nn.LayerNorm,
+            var_num=None,
+    ):
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+
+        self.q = nn.Linear(dim, dim, bias=qkv_bias)
+        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        if var_num is not None:
+            self.template = nn.Parameter(
+                torch.zeros(var_num, dim), requires_grad=True)
+            torch.nn.init.normal_(self.template, std=.02)
+        self.var_num = var_num
+
+    def forward(self, x, query=None):
+        B, N, C = x.shape
+        if query is not None:
+            q = self.q(query).reshape(
+                B, query.shape[1], self.num_heads, self.head_dim).permute(0, 2, 1, 3)
+            q = self.q_norm(q)
+            var_num = query.shape[1]
+        else:
+            q = self.q(self.template).reshape(1, self.var_num,
+                                              self.num_heads, self.head_dim).permute(0, 2, 1, 3)
+            q = self.q_norm(q)
+            q = q.repeat(B, 1, 1, 1)
+            var_num = self.var_num
+        kv = self.kv(x).reshape(B, N, 2, self.num_heads,
+                                self.head_dim).permute(2, 0, 3, 1, 4)
+        k, v = kv.unbind(0)
+        k = self.k_norm(k)
+
+        x = F.scaled_dot_product_attention(
+            q, k, v,
+            dropout_p=self.attn_drop.p if self.training else 0.,
+        )
+
+        x = x.transpose(1, 2).reshape(B, var_num, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class DynamicLinear(nn.Module):
+    """
+    A dynamic linear layer that can interpolate the weight size to support any given input and output feature dimension.
+    """
+
+    def __init__(self, in_features=None, out_features=None, fixed_in=0, bias=True):
+        super(DynamicLinear, self).__init__()
+        assert fixed_in < in_features, "fixed_in < in_features is required !!!"
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weights = nn.Parameter(torch.Tensor(out_features, in_features))
+        self.bias = nn.Parameter(torch.Tensor(out_features))
+        self.fixed_in = fixed_in
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.kaiming_uniform_(self.weights, a=math.sqrt(5))
+        fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weights)
+        bound = 1 / math.sqrt(fan_in)
+        nn.init.uniform_(self.bias, -bound, bound)
+
+    def forward(self, x, out_features):
+        """
+        Forward pass for the dynamic linear layer.
+        """
+        fixed_weights = self.weights[:, :self.fixed_in]
+        dynamic_weights = self.weights[:, self.fixed_in:]
+        this_bias = self.bias
+        in_features = x.shape[-1]
+
+        if in_features != self.weights.size(1) or out_features != self.weights.size(0):
+            dynamic_weights = F.interpolate(dynamic_weights.unsqueeze(0).unsqueeze(0), size=(
+                out_features, in_features-self.fixed_in), mode='bilinear', align_corners=False).squeeze(0).squeeze(0)
+            if self.fixed_in != 0:
+                fixed_weights = F.interpolate(fixed_weights.unsqueeze(0).unsqueeze(0), size=(
+                    out_features, self.fixed_in), mode='bilinear', align_corners=False).squeeze(0).squeeze(0)
+        if out_features != self.weights.size(0):
+            this_bias = F.interpolate(this_bias.unsqueeze(0).unsqueeze(0).unsqueeze(0), size=(
+                1, out_features), mode='bilinear', align_corners=False).squeeze(0).squeeze(0).squeeze(0)
+        return F.linear(x, torch.cat((fixed_weights, dynamic_weights), dim=1), this_bias)
+
+
+class DynamicLinearMlp(nn.Module):
+    def __init__(
+            self,
+            in_features,
+            hidden_features=None,
+            out_features=None,
+            act_layer=nn.GELU,
+            norm_layer=None,
+            bias=True,
+            drop=0.,
+            prefix_token_length=None,
+            group=1,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        bias = to_2tuple(bias)
+        drop_probs = to_2tuple(drop)
+
+        self.fc1 = nn.Conv1d(in_features, hidden_features,
+                             3, groups=group, bias=bias[0], padding=1)
+        self.act = act_layer()
+        self.drop1 = nn.Dropout(drop_probs[0])
+
+        self.norm = norm_layer(
+            hidden_features) if norm_layer is not None else nn.Identity()
+        self.seq_fc = DynamicLinear(
+            hidden_features//4, hidden_features//4, bias=bias[1], fixed_in=prefix_token_length)
+        self.prompt_fc = DynamicLinear(
+            hidden_features//4, prefix_token_length, bias=bias[1], fixed_in=prefix_token_length)
+
+        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
+        self.drop2 = nn.Dropout(drop_probs[1])
+        self.hidden_features = hidden_features
+        self.prefix_token_length = prefix_token_length
+
+    def dynamic_linear(self, x, prefix_seq_len):
+        x_func = x[:, :, prefix_seq_len:]
+        x_seq = x[:, :, :prefix_seq_len]
+        x_seq_out = self.seq_fc(
+            x_seq, x_seq.shape[-1]-self.prefix_token_length)
+        x_prompt = self.prompt_fc(x_seq, self.prefix_token_length)
+        x = torch.cat((x_prompt, x_seq_out, x_func), dim=-1)
+        return x
+
+    def split_dynamic_linear(self, x, prefix_seq_len):
+        x1, x2 = x.chunk(2, dim=-2)
+        x1 = self.dynamic_linear(x1, prefix_seq_len)
+        return torch.cat((x1, x2), dim=-2)
+
+    def forward(self, x, prefix_seq_len, dim=2):
+        n, var, l, c = x.shape
+        x = x.view(-1, l, c)
+        x = x.transpose(-1, -2)
+        x = self.fc1(x)
+        x = self.split_dynamic_linear(x, prefix_seq_len)
+        x = self.act(x)
+        x = self.drop1(x)
+        x = x.transpose(1, 2)
+        x = self.norm(x)
+        x = self.fc2(x).view(n, var, l, c)
+        x = self.drop2(x)
+        return x
+
+
+class LearnablePositionalEmbedding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        super(LearnablePositionalEmbedding, self).__init__()
+        # Compute the positional encodings once in log space.
+        self.pe = nn.Parameter(torch.zeros(
+            1, 1, max_len, d_model), requires_grad=True)
+
+        pe = torch.zeros(max_len, d_model).float()
+        position = torch.arange(0, max_len).float().unsqueeze(1)
+        div_term = (torch.arange(0, d_model, 2).float()
+                    * -(math.log(10000.0) / d_model)).exp()
+
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+
+        pe = pe.unsqueeze(0).unsqueeze(0)
+        self.pe.data.copy_(pe.float())
+        del pe
+
+    def forward(self, x, offset=0):
+        return self.pe[:, :, offset:offset+x.size(2)]
+
+
+class SeqAttention(nn.Module):
+
+    def __init__(
+            self,
+            dim,
+            num_heads=8,
+            qkv_bias=False,
+            qk_norm=False,
+            attn_drop=0.,
+            proj_drop=0.,
+            norm_layer=nn.LayerNorm,
+    ):
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, attn_mask=None):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
+                                  self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+        q, k = self.q_norm(q), self.k_norm(k)
+        x = F.scaled_dot_product_attention(
+            q, k, v,  # attn_mask=attn_mask,
+            dropout_p=self.attn_drop.p if self.training else 0.,
+        )
+
+        x = x.transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class VarAttention(nn.Module):
+
+    def __init__(
+            self,
+            dim,
+            num_heads=8,
+            qkv_bias=False,
+            qk_norm=False,
+            attn_drop=0.,
+            proj_drop=0.,
+            norm_layer=nn.LayerNorm,
+    ):
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, P, C = x.shape
+
+        qkv = self.qkv(x).reshape(B, N, P, 3, self.num_heads,
+                                  self.head_dim).permute(3, 0, 2, 4, 1, 5)
+        q, k, v = qkv.unbind(0)
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        q = q.mean(dim=1, keepdim=False)
+        k = k.mean(dim=1, keepdim=False)
+        v = v.permute(0, 2, 3, 4, 1).reshape(B, self.num_heads, N, -1)
+
+        x = F.scaled_dot_product_attention(
+            q, k, v,
+            dropout_p=self.attn_drop.p if self.training else 0.,
+        )
+
+        x = x.view(B, self.num_heads, N, -1, P).permute(0,
+                                                        2, 4, 1, 3).reshape(B, N, P, -1)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class GateLayer(nn.Module):
+    def __init__(self, dim, init_values=1e-5, inplace=False):
+        super().__init__()
+        self.inplace = inplace
+        self.gate = nn.Linear(dim, 1)
+
+    def forward(self, x):
+        gate_value = self.gate(x)
+        return gate_value.sigmoid() * x
+
+
+class SeqAttBlock(nn.Module):
+
+    def __init__(
+            self,
+            dim,
+            num_heads,
+            qkv_bias=False,
+            qk_norm=False,
+            proj_drop=0.,
+            attn_drop=0.,
+            init_values=None,
+            drop_path=0.,
+            norm_layer=nn.LayerNorm,
+    ):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn_seq = SeqAttention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop,
+            norm_layer=norm_layer,
+        )
+
+        self.ls1 = GateLayer(dim, init_values=init_values)
+        self.drop_path1 = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(self, x, attn_mask):
+        x_input = x
+        x = self.norm1(x)
+        n_vars, n_seqs = x.shape[1], x.shape[2]
+        x = torch.reshape(
+            x, (-1, x.shape[-2], x.shape[-1]))
+        x = self.attn_seq(x, attn_mask)
+        x = torch.reshape(
+            x, (-1, n_vars, n_seqs, x.shape[-1]))
+        x = x_input + self.drop_path1(self.ls1(x))
+        return x
+
+
+class VarAttBlock(nn.Module):
+
+    def __init__(
+            self,
+            dim,
+            num_heads,
+            qkv_bias=False,
+            qk_norm=False,
+            proj_drop=0.,
+            attn_drop=0.,
+            init_values=None,
+            drop_path=0.,
+            norm_layer=nn.LayerNorm,
+    ):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn_var = VarAttention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop,
+            norm_layer=norm_layer,
+        )
+        self.ls1 = GateLayer(dim, init_values=init_values)
+        self.drop_path1 = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(self, x):
+        x = x + self.drop_path1(self.ls1(self.attn_var(self.norm1(x))))
+        return x
+
+
+class MLPBlock(nn.Module):
+
+    def __init__(
+            self,
+            dim,
+            mlp_ratio=4.,
+            proj_drop=0.,
+            init_values=None,
+            drop_path=0.,
+            act_layer=nn.GELU,
+            norm_layer=nn.LayerNorm,
+            mlp_layer=None,
+            prefix_token_length=0,
+    ):
+        super().__init__()
+        self.norm2 = norm_layer(dim)
+        if mlp_layer is DynamicLinearMlp:
+            self.mlp = mlp_layer(
+                in_features=dim,
+                hidden_features=int(dim * mlp_ratio),
+                act_layer=act_layer,
+                drop=proj_drop,
+                prefix_token_length=prefix_token_length,
+            )
+        else:
+            self.mlp = mlp_layer(
+                in_features=dim,
+                hidden_features=int(dim * mlp_ratio),
+                act_layer=act_layer,
+                drop=proj_drop,
+            )
+        self.ls2 = GateLayer(dim, init_values=init_values)
+        self.drop_path2 = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x, prefix_seq_len=None):
+        if prefix_seq_len is not None:
+            x = x + \
+                self.drop_path2(
+                    self.ls2(self.mlp(self.norm2(x), prefix_seq_len=prefix_seq_len)))
+        else:
+            x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
+        return x
+
+
+class BasicBlock(nn.Module):
+    def __init__(
+            self,
+            dim,
+            num_heads,
+            mlp_ratio=8.,
+            qkv_bias=False,
+            qk_norm=False,
+            proj_drop=0.,
+            attn_drop=0.,
+            init_values=None,
+            drop_path=0.,
+            act_layer=nn.GELU,
+            norm_layer=nn.LayerNorm,
+            prefix_token_length=0,
+    ):
+        super().__init__()
+        self.seq_att_block = SeqAttBlock(dim=dim, num_heads=num_heads,
+                                         qkv_bias=qkv_bias, qk_norm=qk_norm,
+                                         attn_drop=attn_drop, init_values=init_values, proj_drop=proj_drop,
+                                         drop_path=drop_path, norm_layer=norm_layer)
+
+        self.var_att_block = VarAttBlock(dim=dim, num_heads=num_heads,
+                                         qkv_bias=qkv_bias, qk_norm=qk_norm,
+                                         attn_drop=attn_drop, init_values=init_values, proj_drop=proj_drop,
+                                         drop_path=drop_path, norm_layer=norm_layer)
+
+        self.dynamic_mlp = MLPBlock(dim=dim, mlp_ratio=mlp_ratio, mlp_layer=DynamicLinearMlp,
+                                    proj_drop=proj_drop, init_values=init_values, drop_path=drop_path,
+                                    act_layer=act_layer, norm_layer=norm_layer,
+                                    prefix_token_length=prefix_token_length)
+
+    def forward(self, x, prefix_seq_len, attn_mask):
+        x = self.seq_att_block(x, attn_mask)
+        x = self.var_att_block(x)
+        x = self.dynamic_mlp(x, prefix_seq_len=prefix_seq_len)
+        return x
+
+
+class PatchEmbedding(nn.Module):
+    def __init__(self, d_model, patch_len, stride, padding, dropout):
+        super(PatchEmbedding, self).__init__()
+        # Patching
+        self.patch_len = patch_len
+        self.stride = stride
+        assert self.patch_len == self.stride, "non-overlap"
+        self.value_embedding = nn.Linear(patch_len, d_model, bias=False)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        n_vars = x.shape[1]
+        x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
+        x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
+        x = self.value_embedding(x)
+        return self.dropout(x), n_vars
+
+
+class CLSHead(nn.Module):
+    def __init__(self, d_model, head_dropout=0):
+        super().__init__()
+        d_mid = d_model
+        self.proj_in = nn.Linear(d_model, d_mid)
+        self.cross_att = CrossAttention(d_mid)
+
+        self.mlp = MLPBlock(dim=d_mid, mlp_ratio=8, mlp_layer=Mlp,
+                            proj_drop=head_dropout, init_values=None, drop_path=0.0,
+                            act_layer=nn.GELU, norm_layer=nn.LayerNorm,
+                            prefix_token_length=None)
+
+    def forward(self, x, category_token=None, return_feature=False):
+        x = self.proj_in(x)
+        B, V, L, C = x.shape
+        x = x.view(-1, L, C)
+        cls_token = x[:, -1:]
+        cls_token = self.cross_att(x, query=cls_token)
+        cls_token = cls_token.reshape(B, V, -1, C)
+
+        cls_token = self.mlp(cls_token)
+        if return_feature:
+            return cls_token
+        m = category_token.shape[2]
+        cls_token = cls_token.expand(B, V, m, C)
+        distance = torch.einsum('nvkc,nvmc->nvm', cls_token, category_token)
+
+        distance = distance.mean(dim=1)
+        return distance
+
+
+class ForecastHead(nn.Module):
+    def __init__(self, d_model, patch_len, stride, pad, head_dropout=0, prefix_token_length=None):
+        super().__init__()
+        d_mid = d_model
+        self.proj_in = nn.Linear(d_model, d_mid)
+        self.mlp = Mlp(
+            in_features=d_model,
+            hidden_features=int(d_model * 4),
+            act_layer=nn.GELU,
+            drop=head_dropout,
+        )
+        self.proj_out = nn.Linear(d_model, patch_len)
+        self.pad = pad
+        self.patch_len = patch_len
+        self.stride = stride
+        self.pos_proj = DynamicLinear(
+            in_features=128, out_features=128, fixed_in=prefix_token_length)
+
+    def forward(self, x_full, pred_len, token_len):
+        x_full = self.proj_in(x_full)
+        x_pred = x_full[:, :, -token_len:]
+        x = x_full.transpose(-1, -2)
+        x = self.pos_proj(x, token_len)
+        x = x.transpose(-1, -2)
+        x = x + x_pred
+        x = self.mlp(x)
+        x = self.proj_out(x)
+
+        bs, n_vars = x.shape[0], x.shape[1]
+        x = x.reshape(-1, x.shape[-2], x.shape[-1])
+        x = x.permute(0, 2, 1)
+        x = torch.nn.functional.fold(x, output_size=(
+            pred_len, 1), kernel_size=(self.patch_len, 1), stride=(self.stride, 1))
+        x = x.squeeze(dim=-1)
+        x = x.reshape(bs, n_vars, -1)
+        x = x.permute(0, 2, 1)
+        return x
+
+
+class Model(nn.Module):
+    """
+    UniTS: Building a Unified Time Series Model
+    """
+
+    def __init__(self, args, configs_list, pretrain=False):
+        super().__init__()
+
+        # (zhenwei) we do not pretrain the model in this stage
+        # if pretrain:
+        #     self.right_prob = args.right_prob
+        #     self.min_mask_ratio = args.min_mask_ratio
+        #     self.max_mask_ratio = args.max_mask_ratio
+
+        # Tokens settings
+        self.num_task = len(configs_list)
+        self.prompt_tokens = nn.ParameterDict({})
+        self.mask_tokens = nn.ParameterDict({})
+        self.cls_tokens = nn.ParameterDict({})
+        self.category_tokens = nn.ParameterDict({})
+
+        for i in range(self.num_task):
+            dataset_name = configs_list[i][1]['dataset']
+            task_data_name = configs_list[i][0]
+            if dataset_name not in self.prompt_tokens:
+                self.prompt_tokens[dataset_name] = torch.zeros(
+                    1, configs_list[i][1]['enc_in'], args.prompt_num, args.d_model)
+                torch.nn.init.normal_(
+                    self.prompt_tokens[dataset_name], std=.02)
+                self.mask_tokens[dataset_name] = torch.zeros(
+                    1, configs_list[i][1]['enc_in'], 1, args.d_model)
+
+            if configs_list[i][1]['task_name'] == 'classification':
+                self.category_tokens[task_data_name] = torch.zeros(
+                    1, configs_list[i][1]['enc_in'], configs_list[i][1]['num_class'], args.d_model)
+                torch.nn.init.normal_(
+                    self.category_tokens[task_data_name], std=.02)
+                self.cls_tokens[task_data_name] = torch.zeros(
+                    1, configs_list[i][1]['enc_in'], 1, args.d_model)
+                torch.nn.init.normal_(self.cls_tokens[task_data_name], std=.02)
+            if pretrain:
+                self.cls_tokens[task_data_name] = torch.zeros(
+                    1, configs_list[i][1]['enc_in'], 1, args.d_model)
+                torch.nn.init.normal_(self.cls_tokens[task_data_name], std=.02)
+
+        self.cls_nums = {}
+        for i in range(self.num_task):
+            task_data_name = configs_list[i][0]
+            if configs_list[i][1]['task_name'] == 'classification':
+                self.cls_nums[task_data_name] = configs_list[i][1]['num_class']
+            elif configs_list[i][1]['task_name'] == 'long_term_forecast':
+                remainder = configs_list[i][1]['seq_len'] % args.patch_len
+                if remainder == 0:
+                    padding = 0
+                else:
+                    padding = args.patch_len - remainder
+                input_token_len = calculate_unfold_output_length(
+                    configs_list[i][1]['seq_len']+padding, args.stride, args.patch_len)
+                input_pad = args.stride * \
+                    (input_token_len - 1) + args.patch_len - \
+                    configs_list[i][1]['seq_len']
+                pred_token_len = calculate_unfold_output_length(
+                    configs_list[i][1]['pred_len']-input_pad, args.stride, args.patch_len)
+                real_len = configs_list[i][1]['seq_len'] + \
+                    configs_list[i][1]['pred_len']
+                self.cls_nums[task_data_name] = [pred_token_len,
+                                                 configs_list[i][1]['pred_len'], real_len]
+
+        self.configs_list = configs_list
+
+        ### model settings ###
+        self.prompt_num = args.prompt_num
+        self.stride = args.stride
+        self.pad = args.stride
+        self.patch_len = args.patch_len
+
+        # input processing
+        self.patch_embeddings = PatchEmbedding(
+            args.d_model, args.patch_len, args.stride, args.stride, args.dropout)
+        self.position_embedding = LearnablePositionalEmbedding(args.d_model)
+        self.prompt2forecat = DynamicLinear(128, 128, fixed_in=args.prompt_num)
+
+        # basic blocks
+        self.block_num = args.e_layers
+        self.blocks = nn.ModuleList(
+            [BasicBlock(dim=args.d_model, num_heads=args.n_heads, qkv_bias=False, qk_norm=False,
+                        mlp_ratio=8., proj_drop=args.dropout, attn_drop=0., drop_path=0.,
+                        init_values=None, prefix_token_length=args.prompt_num) for l in range(args.e_layers)]
+        )
+
+        # output processing
+        self.cls_head = CLSHead(args.d_model, head_dropout=args.dropout)
+        self.forecast_head = ForecastHead(
+            args.d_model, args.patch_len, args.stride, args.stride, prefix_token_length=args.prompt_num, head_dropout=args.dropout)
+        if pretrain:
+            self.pretrain_head = ForecastHead(
+                args.d_model, args.patch_len, args.stride, args.stride, prefix_token_length=1, head_dropout=args.dropout)
+
+    def tokenize(self, x, mask=None):
+        # Normalization from Non-stationary Transformer
+        means = x.mean(1, keepdim=True).detach()
+        x = x - means
+        if mask is not None:
+            x = x.masked_fill(mask == 0, 0)
+            stdev = torch.sqrt(torch.sum(x * x, dim=1) /
+                               torch.sum(mask == 1, dim=1) + 1e-5)
+            stdev = stdev.unsqueeze(dim=1)
+        else:
+            stdev = torch.sqrt(
+                torch.var(x, dim=1, keepdim=True, unbiased=False) + 1e-5)
+        x /= stdev
+        x = x.permute(0, 2, 1)
+        remainder = x.shape[2] % self.patch_len
+        if remainder != 0:
+            padding = self.patch_len - remainder
+            x = F.pad(x, (0, padding))
+        else:
+            padding = 0
+        x, n_vars = self.patch_embeddings(x)
+        return x, means, stdev, n_vars, padding
+
+    def prepare_prompt(self, x, n_vars, prefix_prompt, task_prompt, task_prompt_num, task_name=None, mask=None):
+        x = torch.reshape(
+            x, (-1, n_vars, x.shape[-2], x.shape[-1]))
+        # append prompt tokens
+        this_prompt = prefix_prompt.repeat(x.shape[0], 1, 1, 1)
+
+        if task_name == 'forecast':
+            this_mask_prompt = task_prompt.repeat(
+                x.shape[0], 1, task_prompt_num, 1)
+            init_full_input = torch.cat(
+                (this_prompt, x, this_mask_prompt), dim=-2)
+            init_mask_prompt = self.prompt2forecat(init_full_input.transpose(
+                -1, -2), init_full_input.shape[2]-prefix_prompt.shape[2]).transpose(-1, -2)
+            this_function_prompt = init_mask_prompt[:, :, -task_prompt_num:]
+            x = torch.cat((this_prompt, x, this_function_prompt), dim=2)
+            x[:, :, self.prompt_num:] = x[:, :, self.prompt_num:] + \
+                self.position_embedding(x[:, :, self.prompt_num:])
+        elif task_name == 'classification':
+            this_function_prompt = task_prompt.repeat(x.shape[0], 1, 1, 1)
+            x = x + self.position_embedding(x)
+            x = torch.cat((this_prompt, x, this_function_prompt), dim=2)
+        elif task_name == 'imputation':
+            # fill the masked parts with mask tokens
+            # for imputation, masked is 0, unmasked is 1, so here to reverse mask
+            mask = 1-mask
+            mask = mask.permute(0, 2, 1)
+            mask = self.mark2token(mask)
+            mask_repeat = mask.unsqueeze(dim=-1)
+
+            mask_token = task_prompt
+            mask_repeat = mask_repeat.repeat(1, 1, 1, x.shape[-1])
+            x = x * (1-mask_repeat) + mask_token * mask_repeat
+
+            init_full_input = torch.cat((this_prompt, x), dim=-2)
+            init_mask_prompt = self.prompt2forecat(
+                init_full_input.transpose(-1, -2), x.shape[2]).transpose(-1, -2)
+            # keep the unmasked tokens and fill the masked ones with init_mask_prompt.
+            x = x * (1-mask_repeat) + init_mask_prompt * mask_repeat
+            x = x + self.position_embedding(x)
+            x = torch.cat((this_prompt, x), dim=2)
+        elif task_name == 'anomaly_detection':
+            x = x + self.position_embedding(x)
+            x = torch.cat((this_prompt, x), dim=2)
+
+        return x
+
+    def mark2token(self, x_mark):
+        x_mark = x_mark.unfold(
+            dimension=-1, size=self.patch_len, step=self.stride)
+        x_mark = x_mark.mean(dim=-1)
+        x_mark = (x_mark > 0).float()
+        return x_mark
+
+    def backbone(self, x, prefix_len, seq_len):
+        attn_mask = None
+        for block in self.blocks:
+            x = block(x, prefix_seq_len=prefix_len +
+                      seq_len, attn_mask=attn_mask)
+        return x
+
+    def forecast(self, x, x_mark, task_id):
+        dataset_name = self.configs_list[task_id][1]['dataset']
+        task_data_name = self.configs_list[task_id][0]
+        prefix_prompt = self.prompt_tokens[dataset_name]
+        task_prompt = self.mask_tokens[dataset_name]
+        task_prompt_num = self.cls_nums[task_data_name][0]
+        task_seq_num = self.cls_nums[task_data_name][1]
+        real_seq_len = self.cls_nums[task_data_name][2]
+
+        x, means, stdev, n_vars, _ = self.tokenize(x)
+
+        x = self.prepare_prompt(
+            x, n_vars, prefix_prompt, task_prompt, task_prompt_num, task_name='forecast')
+
+        seq_token_len = x.shape[-2]-prefix_prompt.shape[2]
+        x = self.backbone(x, prefix_prompt.shape[2], seq_token_len)
+
+        x = self.forecast_head(
+            x, real_seq_len, seq_token_len)
+        x = x[:, -task_seq_num:]
+
+        # De-Normalization from Non-stationary Transformer
+        x = x * (stdev[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
+        x = x + (means[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
+
+        return x
+
+    def classification(self, x, x_mark, task_id):
+        dataset_name = self.configs_list[task_id][1]['dataset']
+        task_data_name = self.configs_list[task_id][0]
+        prefix_prompt = self.prompt_tokens[dataset_name]
+        task_prompt = self.cls_tokens[task_data_name]
+        task_prompt_num = 1
+        category_token = self.category_tokens[task_data_name]
+
+        x, means, stdev, n_vars, _ = self.tokenize(x)
+
+        seq_len = x.shape[-2]
+
+        x = self.prepare_prompt(
+            x, n_vars, prefix_prompt, task_prompt, task_prompt_num, task_name='classification')
+
+        x = self.backbone(x, prefix_prompt.shape[2], seq_len)
+
+        x = self.cls_head(x, category_token)
+
+        return x
+
+    def imputation(self, x, x_mark, mask, task_id):
+        dataset_name = self.configs_list[task_id][1]['dataset']
+        prefix_prompt = self.prompt_tokens[dataset_name]
+        task_prompt = self.mask_tokens[dataset_name]
+
+        seq_len = x.shape[1]
+        x, means, stdev, n_vars, padding = self.tokenize(x, mask)
+
+        x = self.prepare_prompt(
+            x, n_vars, prefix_prompt, task_prompt, None, mask=mask, task_name='imputation')
+        seq_token_len = x.shape[-2]-prefix_prompt.shape[2]
+        x = self.backbone(x, prefix_prompt.shape[2], seq_token_len)
+
+        x = self.forecast_head(
+            x, seq_len+padding, seq_token_len)
+        x = x[:, :seq_len]
+
+        # De-Normalization from Non-stationary Transformer
+        x = x * (stdev[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
+        x = x + (means[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
+
+        return x
+
+    def anomaly_detection(self, x, x_mark, task_id):
+        dataset_name = self.configs_list[task_id][1]['dataset']
+        prefix_prompt = self.prompt_tokens[dataset_name]
+
+        seq_len = x.shape[1]
+        x, means, stdev, n_vars, padding = self.tokenize(x)
+
+        x = self.prepare_prompt(x, n_vars, prefix_prompt,
+                                None, None, task_name='anomaly_detection')
+        seq_token_len = x.shape[-2]-prefix_prompt.shape[2]
+        x = self.backbone(x, prefix_prompt.shape[2], seq_token_len)
+
+        x = self.forecast_head(
+            x, seq_len+padding, seq_token_len)
+        x = x[:, :seq_len]
+
+        # De-Normalization from Non-stationary Transformer
+        x = x * (stdev[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
+        x = x + (means[:, 0, :].unsqueeze(1).repeat(1, x.shape[1], 1))
+
+        return x
+
+    def random_masking(self, x, min_mask_ratio, max_mask_ratio):
+        """
+        Perform per-sample random masking.
+        """
+        N, V, L, D = x.shape  # batch, var, length, dim
+
+        # Calculate mask ratios and lengths to keep for each sample in the batch
+        mask_ratios = torch.rand(N, device=x.device) * \
+            (max_mask_ratio - min_mask_ratio) + min_mask_ratio
+        len_keeps = (L * (1 - mask_ratios)).long()
+
+        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]
+
+        # sort noise for each sample
+        # ascend: small is keep, large is remove
+        ids_shuffle = torch.argsort(noise, dim=1)
+        ids_restore = torch.argsort(ids_shuffle, dim=1)
+
+        # generate the binary mask: 0 is keep, 1 is remove
+        mask = torch.ones([N, L], device=x.device)
+
+        # Create a range tensor and compare with len_keeps for mask generation
+        range_tensor = torch.arange(L, device=x.device).expand(N, L)
+        mask = (range_tensor >= len_keeps.unsqueeze(1))
+
+        # unshuffle to get the binary mask
+        mask = torch.gather(mask, dim=1, index=ids_restore)
+        mask = mask.float()
+
+        return mask
+
+    def right_masking(self, x, min_mask_ratio, max_mask_ratio):
+        N, V, L, D = x.shape  # batch, var, length, dim
+
+        # Randomly choose a mask ratio for each sample within the specified range
+        mask_ratios = torch.rand(N, device=x.device) * \
+            (max_mask_ratio - min_mask_ratio) + min_mask_ratio
+        len_keeps = (L * (1 - mask_ratios)).long()
+
+        # Binary mask creation without a for loop
+        len_keeps_matrix = len_keeps.unsqueeze(1).expand(N, L)
+        indices = torch.arange(L, device=x.device).expand_as(len_keeps_matrix)
+        mask = indices >= len_keeps_matrix
+        mask = mask.float()
+
+        return mask
+
+    def choose_masking(self, x, right_prob, min_mask_ratio, max_mask_ratio):
+        # Generate a random number to decide which masking function to use
+        if torch.rand(1).item() > right_prob:
+            return self.random_masking(x, min_mask_ratio, max_mask_ratio)
+        else:
+            return self.right_masking(x, min_mask_ratio, max_mask_ratio)
+
+    def get_mask_seq(self, mask, seq_len):
+        mask_seq = mask.unsqueeze(dim=-1).repeat(1, 1, self.patch_len)
+        mask_seq = mask_seq.permute(0, 2, 1)
+        mask_seq = mask_seq.masked_fill(mask_seq == 0, -1e9)
+        # Fold operation
+        mask_seq = torch.nn.functional.fold(mask_seq, output_size=(
+            seq_len, 1), kernel_size=(self.patch_len, 1), stride=(self.stride, 1))
+        # Apply threshold to bring back to 0/1 values
+        mask_seq = (mask_seq > 0).float()
+        mask_seq = mask_seq.squeeze(dim=-1).squeeze(dim=1)
+        return mask_seq
+
+    def pretraining(self, x, x_mark, task_id, enable_mask=False):
+        dataset_name = self.configs_list[task_id][1]['dataset']
+        task_data_name = self.configs_list[task_id][0]
+        prefix_prompt = self.prompt_tokens[dataset_name]
+        mask_token = self.mask_tokens[dataset_name]
+        cls_token = self.cls_tokens[task_data_name]
+
+        seq_len = x.shape[1]
+        x, means, stdev, n_vars, padding = self.tokenize(x)
+        seq_token_len = x.shape[-2]
+
+        # append prompt tokens
+        x = torch.reshape(
+            x, (-1, n_vars, x.shape[-2], x.shape[-1]))
+        # prepare prompts
+        this_prompt = prefix_prompt.repeat(x.shape[0], 1, 1, 1)
+
+        if enable_mask:
+            mask = self.choose_masking(x, self.right_prob,
+                                       self.min_mask_ratio, self.max_mask_ratio)
+            mask_repeat = mask.unsqueeze(dim=1).unsqueeze(dim=-1)
+            mask_repeat = mask_repeat.repeat(1, x.shape[1], 1, x.shape[-1])
+            x = x * (1-mask_repeat) + mask_token * mask_repeat  # todo
+
+            init_full_input = torch.cat((this_prompt, x), dim=-2)
+            init_mask_prompt = self.prompt2forecat(
+                init_full_input.transpose(-1, -2), x.shape[2]).transpose(-1, -2)
+            # keep the unmasked tokens and fill the masked ones with init_mask_prompt.
+            x = x * (1-mask_repeat) + init_mask_prompt * mask_repeat
+            x = x + self.position_embedding(x)
+            mask_seq = self.get_mask_seq(mask, seq_len+padding)
+            mask_seq = mask_seq[:, :seq_len]
+        this_function_prompt = cls_token.repeat(x.shape[0], 1, 1, 1)
+        x = torch.cat((this_prompt, x, this_function_prompt), dim=2)
+
+        x = self.backbone(x, prefix_prompt.shape[2], seq_token_len)
+
+        if enable_mask:
+            mask_dec_out = self.forecast_head(
+                x[:, :, :-1], seq_len+padding, seq_token_len)
+            mask_dec_out = mask_dec_out[:, :seq_len]
+            # De-Normalization from Non-stationary Transformer
+            mask_dec_out = mask_dec_out * \
+                (stdev[:, 0, :].unsqueeze(1).repeat(
+                    1, mask_dec_out.shape[1], 1))
+            mask_dec_out = mask_dec_out + \
+                (means[:, 0, :].unsqueeze(1).repeat(
+                    1, mask_dec_out.shape[1], 1))
+            cls_dec_out = self.cls_head(x, return_feature=True)
+            # detach grad of the forecasting on tokens
+            fused_dec_out = torch.cat(
+                (cls_dec_out, x[:, :, self.prompt_num:-1].detach()), dim=2)
+            cls_dec_out = self.pretrain_head(
+                fused_dec_out, seq_len+padding, seq_token_len)
+            cls_dec_out = cls_dec_out[:, :seq_len]
+            cls_dec_out = cls_dec_out * \
+                (stdev[:, 0, :].unsqueeze(1).repeat(
+                    1, cls_dec_out.shape[1], 1))
+            cls_dec_out = cls_dec_out + \
+                (means[:, 0, :].unsqueeze(1).repeat(
+                    1, cls_dec_out.shape[1], 1))
+
+            return cls_dec_out, mask_dec_out, mask_seq
+        else:
+            return cls_dec_out
+
+    def forward(self, x_enc, x_mark_enc, x_dec=None, x_mark_dec=None,
+                mask=None, task_id=None, task_name=None, enable_mask=None):
+        task_id = 0
+
+        # if task_name == 'long_term_forecast' or task_name == 'short_term_forecast':
+        dec_out = self.forecast(x_enc, x_mark_enc, task_id)
+        return dec_out  # [B, L, D]
+        # if task_name == 'imputation':
+        #     dec_out = self.imputation(
+        #         x_enc, x_mark_enc, mask, task_id)
+        #     return dec_out  # [B, L, D]
+        # if task_name == 'anomaly_detection':
+        #     dec_out = self.anomaly_detection(x_enc, x_mark_enc, task_id)
+        #     return dec_out  # [B, L, D]
+        # if task_name == 'classification':
+        #     dec_out = self.classification(x_enc, x_mark_enc, task_id)
+        #     return dec_out  # [B, N]
+        # if 'pretrain' in task_name:
+        #     dec_out = self.pretraining(x_enc, x_mark_enc, task_id,
+        #                                enable_mask=enable_mask)
+        #     return dec_out
+        # return None
+
+
+class UniTS(Forecaster):
+    def __init__(
+        self,
+        ckpt_path: str = None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.no_training = True
+
+        args, configs_list = self.generate_units_default_args()
+        self.model = Model(args, configs_list, pretrain=False)
+        
+        pretrain_weight_path = ckpt_path
+        ckpt = torch.load(pretrain_weight_path, map_location='cuda:0')
+        msg = self.model.load_state_dict(ckpt, strict=False)
+        print('load pretrained model:', pretrain_weight_path)
+        # print(msg)
+
+    def generate_units_default_args(self):
+        class Args:
+            def __init__(self):
+                self.d_model = 128
+                self.n_heads = 8
+                self.e_layers = 3
+                self.prompt_num = 10
+                self.dropout = 0.1
+                self.patch_len = 16
+                self.stride = 16
+                self.batch_size = 32
+
+        args = Args()
+
+        task_data_config = {
+            "LTF_ETTm2_p96": {
+                "task_name": "long_term_forecast",
+                "dataset": "ETTm2",
+                "data": "ETTm2",
+                "embed": "timeF",
+                "root_path": "./dataset/ETT-small/",
+                "data_path": "ETTm2.csv",
+                "features": "M",
+                "seq_len": self.context_length,
+                "label_len": 48,
+                "pred_len": self.prediction_length,
+                "enc_in": self.target_dim,
+                "dec_in": self.target_dim,
+                "c_out": self.target_dim
+            }
+        }
+        task_data_config_list = []
+        for task_name, task_config in task_data_config.items():
+            task_config['max_batch'] = args.batch_size
+            task_data_config_list.append([task_name, task_config])
+        return args, task_data_config_list
+
+
+    def forecast(self, batch_data, pred_len=None, dataset_name=None, *args, **kwargs):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = inputs[:, -self.context_length:]
+        B, _, K = inputs.shape
+        point_forecast = self.model.forward(inputs, None)
+        return point_forecast.unsqueeze(1)
diff --git a/probts/model/forecaster/prob_forecaster/__init__.py b/probts/model/forecaster/prob_forecaster/__init__.py
new file mode 100644
index 0000000..49c2d84
--- /dev/null
+++ b/probts/model/forecaster/prob_forecaster/__init__.py
@@ -0,0 +1,40 @@
+from .gru_nvp import GRU_NVP
+from .gru_maf import GRU_MAF
+from .timegrad import TimeGrad
+from .trans_maf import Trans_MAF
+from .csdi import CSDI
+from .tsdiff import TSDiffCond
+
+# ------- add lag_llama to sys.path ---------
+try:
+    import os, sys
+    current_dir = os.path.dirname(os.path.realpath(__file__))
+    project_root = os.path.abspath(os.path.join(current_dir, '..', '..', '..', '..'))
+    lag_llama_path = os.path.join(project_root, 'submodules', 'lag_llama')
+    moirai_path = os.path.join(project_root, 'submodules', 'uni2ts', 'src')
+
+    if lag_llama_path not in sys.path:
+        sys.path.append(lag_llama_path)
+
+    if moirai_path not in sys.path:
+        sys.path.append(moirai_path)
+
+except Exception as e:
+    print(f"Warning: Unable to add lag_llama to sys.path. {e}")
+# -------------------------------------------
+
+import importlib
+
+modules = [
+    ('moirai', 'Moirai'),
+    ('chronos', 'Chronos'),
+    ('lag_llama', 'LagLlama'),
+]
+
+for module, class_name in modules:
+    try:
+        mod = importlib.import_module(f".{module}", package=__package__)
+        globals()[class_name] = getattr(mod, class_name)
+    except ImportError:
+        # print(f"Warning: {class_name} is not available due to missing dependencies.")
+        pass
\ No newline at end of file
diff --git a/probts/model/forecaster/prob_forecaster/chronos.py b/probts/model/forecaster/prob_forecaster/chronos.py
new file mode 100644
index 0000000..5d03ef8
--- /dev/null
+++ b/probts/model/forecaster/prob_forecaster/chronos.py
@@ -0,0 +1,66 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from Chronos
+# - Source: https://github.com/amazon-science/chronos-forecasting
+# - Paper: Chronos: Learning the Language of Time Series
+# - License: Apache License 2.0
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+from chronos import ChronosPipeline
+from einops import rearrange
+
+from probts.model.forecaster import Forecaster
+
+
+class Chronos(Forecaster):
+    def __init__(
+        self,
+        model_size: str = 'base',
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        
+        # debug
+        model_size = 'small'
+        self.pred_len = kwargs.get('prediction_length')
+
+        # Load pretrained model
+        self.no_training = True
+        # Load Chronos
+        self.pipeline = ChronosPipeline.from_pretrained(
+            "amazon/chronos-t5-{}".format(model_size),
+            device_map="cuda",
+            torch_dtype=torch.bfloat16,
+        )
+
+
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = inputs[:, -self.context_length:]
+        
+        B, _, K = inputs.shape
+        inputs = rearrange(inputs, 'b l k -> (b k) l').cpu()
+        context = [inputs[i] for i in range(B*K)]
+        inner_batch_size = 12 # for 80G gpu
+        forecast_samples = []
+
+        # Process in batches of size `inner_batch_size`
+        for i in range(0, len(context), inner_batch_size):
+            batch_context = context[i:i + inner_batch_size]
+            batch_forecast_samples = self.pipeline.predict(
+                batch_context,
+                prediction_length=self.pred_len,
+                num_samples=num_samples,
+                limit_prediction_length=False
+            )
+            forecast_samples.append(batch_forecast_samples)
+        
+        forecast_samples = torch.cat(forecast_samples, dim=0)
+        prob_forecast = rearrange(forecast_samples, '(b k) s l -> b s l k', b=B, k=K)
+        
+        return prob_forecast
+
+
diff --git a/probts/model/forecaster/prob_forecaster/csdi.py b/probts/model/forecaster/prob_forecaster/csdi.py
new file mode 100644
index 0000000..91042bc
--- /dev/null
+++ b/probts/model/forecaster/prob_forecaster/csdi.py
@@ -0,0 +1,200 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from CSDI
+# - Source: https://github.com/ermongroup/CSDI
+# - Paper: CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
+# - License: MIT license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import repeat
+from probts.model.forecaster import Forecaster
+from probts.model.nn.layers import diff_CSDI
+
+
+class CSDI(Forecaster):
+    def __init__(
+        self, 
+        channels: int = 64,
+        emb_time_dim: int = 128,
+        emb_feature_dim: int = 16,
+        num_steps: int = 50,
+        schedule: str = "quad",
+        beta_start: float = 0.0001,
+        beta_end: float = 0.5,
+        diffusion_embedding_dim: int = 128,
+        num_heads: int = 8,
+        n_layers: int = 4,
+        sample_size: int = 64,
+        linear_trans: bool = False,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.autoregressive = False
+        self.dist_args = nn.Identity()
+
+        self.emb_time_dim = emb_time_dim
+        self.emb_feature_dim = emb_feature_dim
+        self.emb_total_dim = self.emb_time_dim + self.emb_feature_dim
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        self.emb_total_dim += 1  # for conditional mask
+        self.embed_layer = nn.Embedding(
+            num_embeddings=self.target_dim, embedding_dim=self.emb_feature_dim
+        )
+        side_dim = self.emb_total_dim
+        self.sample_size = sample_size
+
+        input_dim = 2
+        self.diffmodel = diff_CSDI(channels, diffusion_embedding_dim, side_dim, num_steps, num_heads, n_layers, inputdim=input_dim,linear=linear_trans)
+
+        # parameters for diffusion models
+        self.num_steps = num_steps
+        if schedule == "quad":
+            self.beta = np.linspace(
+                beta_start ** 0.5, beta_end ** 0.5, self.num_steps
+            ) ** 2
+        elif schedule == "linear":
+            self.beta = np.linspace(
+                beta_start, beta_end, self.num_steps
+            )
+
+        self.alpha_hat = 1 - self.beta
+        self.alpha = np.cumprod(self.alpha_hat)
+        self.alpha_torch = torch.tensor(self.alpha).float().unsqueeze(1).unsqueeze(1).to(self.device)
+
+    def time_embedding(self, pos, device, d_model=128):
+        pe = torch.zeros(pos.shape[0], pos.shape[1], d_model).to(device)
+        position = pos.unsqueeze(2)
+        div_term = 1 / torch.pow(
+            10000.0, torch.arange(0, d_model, 2).to(device) / d_model
+        )
+        pe[:, :, 0::2] = torch.sin(position * div_term)
+        pe[:, :, 1::2] = torch.cos(position * div_term)
+        return pe
+
+    def set_input_to_diffmodel(self, noisy_data, observed_data, cond_mask):
+        cond_obs = (cond_mask * observed_data).unsqueeze(1)
+        noisy_target = ((1 - cond_mask) * noisy_data).unsqueeze(1)
+        total_input = torch.cat([cond_obs, noisy_target], dim=1)  # (B,2,K,L)
+        return total_input
+
+    def get_masks(self, batch_data):
+        hist_observed_mask = batch_data.past_observed_values[:, -self.context_length:, ...]
+        target_observed_mask = batch_data.future_observed_values
+        observed_mask = torch.cat((hist_observed_mask, target_observed_mask), dim=1)
+
+        cond_mask = torch.cat((hist_observed_mask, torch.zeros_like(target_observed_mask)), dim=1)
+        return observed_mask, cond_mask # [B L K]
+
+    def get_side_info(self, observed_data, cond_mask, target_dimension_indicator, observed_tp=None):
+        
+        B, K, L = observed_data.shape
+        if observed_tp is None:
+            observed_tp = torch.arange(L) * 1.0
+            observed_tp = repeat(observed_tp, 'l -> b l', b=B).to(observed_data.device)
+
+        time_embed = self.time_embedding(observed_tp, observed_data.device, self.emb_time_dim)  # (B,L,emb)
+        time_embed = time_embed.unsqueeze(2).expand(-1, -1, K, -1) # (B,L,K, emb)
+        feature_embed = self.embed_layer(target_dimension_indicator)  # (B, K,emb)
+        feature_embed = feature_embed.unsqueeze(1).expand(-1, L, -1, -1) # (B,L,K, emb)
+
+        side_info = torch.cat([time_embed, feature_embed], dim=-1)  # (B,L,K,*)
+        side_info = side_info.permute(0, 3, 2, 1)  # (B,*,K,L)
+        side_mask = cond_mask.unsqueeze(1)  # (B,1,K,L)
+
+        side_info = torch.cat([side_info, side_mask], dim=1)
+        return side_info # (B,D,K,L)
+
+    def loss(self, batch_data, observed_tp=None):
+        past_target_cdf = batch_data.past_target_cdf[:, -self.context_length:, ...]
+        future_target_cdf = batch_data.future_target_cdf
+
+        observed_data = torch.cat([past_target_cdf, future_target_cdf], dim=1)
+        B, L, K = observed_data.shape
+        t = torch.randint(0, self.num_steps, [B]).to(past_target_cdf.device)
+
+        observed_mask, gt_mask = self.get_masks(batch_data)
+        feature_id = batch_data.target_dimension_indicator
+
+        if K > self.sample_size:
+            # sample subset
+            sampled_data = []
+            sampled_mask = []
+            sampled_feature_id = []
+            sampled_gt_mask = []
+            for i in range(len(observed_data)):
+                ind = np.arange(K)
+                np.random.shuffle(ind)
+                sampled_data.append(observed_data[i,...,ind[:self.sample_size]])
+                sampled_mask.append(observed_mask[i,...,ind[:self.sample_size]])
+                sampled_feature_id.append(feature_id[i,ind[:self.sample_size]])
+                sampled_gt_mask.append(gt_mask[i,...,ind[:self.sample_size]])
+            observed_data = torch.stack(sampled_data,0)
+            observed_mask = torch.stack(sampled_mask,0)
+            feature_id = torch.stack(sampled_feature_id,0)
+            gt_mask = torch.stack(sampled_gt_mask,0)
+
+        observed_data = observed_data.permute(0,2,1) # [B K L]
+        observed_mask = observed_mask.permute(0,2,1) # [B K L]
+        cond_mask = gt_mask.permute(0,2,1) # [B K L]
+
+        side_info = self.get_side_info(observed_data, cond_mask, feature_id, observed_tp)
+
+        target_mask = observed_mask - cond_mask
+        current_alpha = self.alpha_torch[t]  # (B,1,1)
+        noise = torch.randn_like(observed_data).to(observed_data.device)
+        noisy_data = (current_alpha ** 0.5) * observed_data + (1.0 - current_alpha) ** 0.5 * noise
+
+
+        total_input = self.set_input_to_diffmodel(noisy_data, observed_data, cond_mask)
+
+        predicted = self.diffmodel(total_input, side_info, t)  # (B,K,L)
+        residual = (noise - predicted) * target_mask
+
+        num_eval = target_mask.sum()
+        loss = (residual ** 2).sum() / (num_eval if num_eval > 0 else 1)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples):
+        observed_data = torch.cat([batch_data.past_target_cdf[:, -self.context_length:, ...], torch.zeros_like(batch_data.future_target_cdf)], dim=1).permute(0,2,1) 
+        _, cond_mask = self.get_masks(batch_data)
+        cond_mask = cond_mask.permute(0,2,1)
+        side_info = self.get_side_info(observed_data, cond_mask, batch_data.target_dimension_indicator)
+        sample = self.sample(observed_data, cond_mask, side_info, num_samples)
+        sample = sample.permute(0,1,3,2)
+        return sample[:, : , -self.prediction_length:, :] # [B N L K]
+
+    def sample(self, observed_data, cond_mask, side_info, n_samples):
+        B, K, L = observed_data.shape
+        imputed_samples = torch.zeros(B, n_samples, K, L).to(observed_data.device)
+
+        for i in range(n_samples):
+            current_sample = torch.randn_like(observed_data).to(observed_data.device)
+
+            for t in range(self.num_steps - 1, -1, -1):
+                cond_obs = (cond_mask * observed_data).unsqueeze(1)
+                noisy_target = ((1 - cond_mask) * current_sample).unsqueeze(1) # [B 1 K L]
+                diff_input = torch.cat([cond_obs, noisy_target], dim=1)  # (B,2,K,L)
+                predicted = self.diffmodel(diff_input, side_info, torch.tensor([t]).to(observed_data.device))
+
+                coeff1 = 1 / self.alpha_hat[t] ** 0.5
+                coeff2 = (1 - self.alpha_hat[t]) / (1 - self.alpha[t]) ** 0.5
+                current_sample = coeff1 * (current_sample - coeff2 * predicted)
+
+                if t > 0:
+                    noise = torch.randn_like(current_sample).to(observed_data.device)
+                    sigma = (
+                        (1.0 - self.alpha[t - 1]) / (1.0 - self.alpha[t]) * self.beta[t]
+                    ) ** 0.5
+                    current_sample += sigma * noise
+
+            imputed_samples[:, i] = current_sample.detach()
+        return imputed_samples
+
+
diff --git a/probts/model/forecaster/prob_forecaster/gru_maf.py b/probts/model/forecaster/prob_forecaster/gru_maf.py
new file mode 100644
index 0000000..58ab17e
--- /dev/null
+++ b/probts/model/forecaster/prob_forecaster/gru_maf.py
@@ -0,0 +1,114 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PyTorch-TS
+# - Source: https://github.com/zalandoresearch/pytorch-ts
+# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
+# - License: MIT, Apache-2.0 license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+
+from probts.data import ProbTSBatchData
+from probts.utils import repeat
+from probts.model.forecaster import Forecaster
+from probts.model.nn import MAF
+
+
+class GRU_MAF(Forecaster):
+    def __init__(
+        self,
+        enc_num_layers: int = 2,
+        enc_hidden_size: int = 40,
+        enc_dropout: float = 0.1,
+        n_blocks: int = 4,
+        hidden_size: int = 100,
+        n_hidden: int = 2,
+        conditional_length: int = 200,
+        dequantize: bool = False,
+        batch_norm: bool = True,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.autoregressive = True
+        
+        self.encoder = nn.GRU(
+            input_size=self.input_size,
+            hidden_size=enc_hidden_size,
+            num_layers=enc_num_layers,
+            dropout=enc_dropout,
+            batch_first=True
+        )
+        self.prob_model = MAF(
+            n_blocks=n_blocks,
+            target_dim=self.target_dim,
+            hidden_size=hidden_size,
+            n_hidden=n_hidden,
+            f_hidden_size=enc_hidden_size,
+            conditional_length=conditional_length,
+            dequantize=dequantize,
+            batch_norm=batch_norm
+        )
+
+    def loss(self, batch_data):
+        if self.use_scaling:
+            self.get_scale(batch_data)
+            self.prob_model.scale = self.scaler.scale
+        
+        inputs = self.get_inputs(batch_data, 'all')
+        enc_outs, states = self.encoder(inputs)
+        enc_outs = enc_outs[:, -self.prediction_length-1:-1, ...]
+        
+        dist_args = self.prob_model.dist_args(enc_outs)
+        loss = self.prob_model.loss(batch_data.future_target_cdf, dist_args).unsqueeze(-1)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        if self.use_scaling:
+            self.get_scale(batch_data)
+        
+        states = self.encode(batch_data)
+        
+        repeated_target_dimension_indicator = repeat(batch_data.target_dimension_indicator, num_samples)
+        repeated_past_target_cdf = repeat(batch_data.past_target_cdf, num_samples)
+        repeated_future_time_feat = repeat(batch_data.future_time_feat, num_samples)
+        repeated_states = repeat(states, num_samples, dim=1)
+        if self.use_scaling:
+            repeated_scale = repeat(self.scaler.scale, num_samples)
+            self.scaler.scale = repeated_scale
+            self.prob_model.scale = repeated_scale
+
+        future_samples = []
+        for k in range(self.prediction_length):
+            repeated_batch_data = ProbTSBatchData({
+                'target_dimension_indicator': repeated_target_dimension_indicator,
+                'past_target_cdf': repeated_past_target_cdf,
+                'future_time_feat': repeated_future_time_feat[:, k:k+1, ...]
+            }, device=batch_data.device)
+
+            enc_outs, repeated_states = self.decode(repeated_batch_data, repeated_states)
+            # Sample
+            dist_args = self.prob_model.dist_args(enc_outs)
+            new_samples = self.prob_model.sample(cond=dist_args)
+            future_samples.append(new_samples)
+
+            repeated_past_target_cdf = torch.cat(
+                (repeated_past_target_cdf, new_samples), dim=1
+            )
+
+        forecasts = torch.cat(future_samples, dim=1).reshape(
+            -1, num_samples, self.prediction_length, self.target_dim)
+        return forecasts
+
+    def encode(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        outputs, states = self.encoder(inputs)
+        return states
+
+    def decode(self, batch_data, states=None):
+        inputs = self.get_inputs(batch_data, 'decode')
+        outputs, states = self.encoder(inputs, states)
+        return outputs, states
diff --git a/probts/model/forecaster/prob_forecaster/gru_nvp.py b/probts/model/forecaster/prob_forecaster/gru_nvp.py
new file mode 100644
index 0000000..1c00f0f
--- /dev/null
+++ b/probts/model/forecaster/prob_forecaster/gru_nvp.py
@@ -0,0 +1,114 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PyTorch-TS
+# - Source: https://github.com/zalandoresearch/pytorch-ts
+# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
+# - License: MIT, Apache-2.0 license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+
+from probts.data import ProbTSBatchData
+from probts.utils import repeat
+from probts.model.forecaster import Forecaster
+from probts.model.nn import RealNVP
+
+
+class GRU_NVP(Forecaster):
+    def __init__(
+        self,
+        enc_num_layers: int = 2,
+        enc_hidden_size: int = 40,
+        enc_dropout: float = 0.1,
+        n_blocks: int = 4,
+        hidden_size: int = 100,
+        n_hidden: int = 2,
+        conditional_length: int = 200,
+        dequantize: bool = False,
+        batch_norm: bool = True,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.autoregressive = True
+        
+        self.encoder = nn.GRU(
+            input_size=self.input_size,
+            hidden_size=enc_hidden_size,
+            num_layers=enc_num_layers,
+            dropout=enc_dropout,
+            batch_first=True
+        )
+        self.prob_model = RealNVP(
+            n_blocks=n_blocks,
+            target_dim=self.target_dim,
+            hidden_size=hidden_size,
+            n_hidden=n_hidden,
+            f_hidden_size=enc_hidden_size,
+            conditional_length=conditional_length,
+            dequantize=dequantize,
+            batch_norm=batch_norm
+        )
+
+    def loss(self, batch_data):
+        if self.use_scaling:
+            self.get_scale(batch_data)
+            self.prob_model.scale = self.scaler.scale
+        
+        inputs = self.get_inputs(batch_data, 'all')
+        enc_outs, states = self.encoder(inputs)
+        enc_outs = enc_outs[:, -self.prediction_length-1:-1, ...]
+        
+        dist_args = self.prob_model.dist_args(enc_outs)
+        loss = self.prob_model.loss(batch_data.future_target_cdf, dist_args).unsqueeze(-1)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        if self.use_scaling:
+            self.get_scale(batch_data)
+        
+        states = self.encode(batch_data)
+        
+        repeated_target_dimension_indicator = repeat(batch_data.target_dimension_indicator, num_samples)
+        repeated_past_target_cdf = repeat(batch_data.past_target_cdf, num_samples)
+        repeated_future_time_feat = repeat(batch_data.future_time_feat, num_samples)
+        repeated_states = repeat(states, num_samples, dim=1)
+        if self.use_scaling:
+            repeated_scale = repeat(self.scaler.scale, num_samples)
+            self.scaler.scale = repeated_scale
+            self.prob_model.scale = repeated_scale
+
+        future_samples = []
+        for k in range(self.prediction_length):
+            repeated_batch_data = ProbTSBatchData({
+                'target_dimension_indicator': repeated_target_dimension_indicator,
+                'past_target_cdf': repeated_past_target_cdf,
+                'future_time_feat': repeated_future_time_feat[:, k:k+1, ...]
+            }, device=batch_data.device)
+
+            enc_outs, repeated_states = self.decode(repeated_batch_data, repeated_states)
+            # Sample
+            dist_args = self.prob_model.dist_args(enc_outs)
+            new_samples = self.prob_model.sample(cond=dist_args)
+            future_samples.append(new_samples)
+
+            repeated_past_target_cdf = torch.cat(
+                (repeated_past_target_cdf, new_samples), dim=1
+            )
+
+        forecasts = torch.cat(future_samples, dim=1).reshape(
+            -1, num_samples, self.prediction_length, self.target_dim)
+        return forecasts
+
+    def encode(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        outputs, states = self.encoder(inputs)
+        return states
+
+    def decode(self, batch_data, states=None):
+        inputs = self.get_inputs(batch_data, 'decode')
+        outputs, states = self.encoder(inputs, states)
+        return outputs, states
diff --git a/probts/model/forecaster/prob_forecaster/lag_llama.py b/probts/model/forecaster/prob_forecaster/lag_llama.py
new file mode 100644
index 0000000..2c1c9a7
--- /dev/null
+++ b/probts/model/forecaster/prob_forecaster/lag_llama.py
@@ -0,0 +1,86 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from lag-llama
+# - Source: https://github.com/time-series-foundation-models/lag-llama
+# - Paper: Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
+# - License: Apache License 2.0
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import numpy as np
+import torch
+
+from gluonts.dataset.common import ListDataset
+
+from probts.model.forecaster import Forecaster
+from submodules.lag_llama.lag_llama.gluon.estimator import LagLlamaEstimator
+
+
+class LagLlama(Forecaster):
+    def __init__(
+        self,
+        use_rope_scaling: bool = True,
+        ckpt_path: str = '/data/Blob_EastUS/v-zhenwzhang/TsFM_checkpoints/lagllama/lag-llama.ckpt',
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        
+        self.ctx_len = kwargs.get('context_length')
+        self.pred_len = kwargs.get('prediction_length')
+
+        # Load pretrained model
+        self.no_training = True
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        ckpt = torch.load(ckpt_path, map_location=device)
+        estimator_args = ckpt["hyper_parameters"]["model_kwargs"]
+        rope_scaling_arguments = {
+            "type": "linear",
+            "factor": max(1.0, (self.ctx_len + self.pred_len) / estimator_args["context_length"]), # 32
+        }
+        # Load model checkpoint
+        estimator = LagLlamaEstimator(
+            ckpt_path=ckpt_path,
+            prediction_length=self.pred_len,
+            context_length=self.ctx_len, # Lag-Llama was trained with a context length of 32, but can work with any context length
+
+            # estimator args
+            input_size=estimator_args["input_size"], # 1
+            n_layer=estimator_args["n_layer"], # 8
+            n_embd_per_head=estimator_args["n_embd_per_head"], # 16
+            n_head=estimator_args["n_head"], # 9
+            scaling=estimator_args["scaling"], # robust
+            time_feat=estimator_args["time_feat"], # True
+            rope_scaling=rope_scaling_arguments if use_rope_scaling else None, # long-term set to True
+
+            batch_size=4,
+            num_parallel_samples=100,
+            device=device,
+        )
+
+        lightning_module = estimator.create_lightning_module()
+        transformation = estimator.create_transformation()
+        self.predictor = estimator.create_predictor(transformation, lightning_module)
+
+    
+    def forecast(self, batch_data, num_samples=None):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = inputs[:, -self.context_length:]
+        datastamps = batch_data.past_time_feat.cpu().numpy().astype('datetime64[s]')
+
+        # for now, we only support batch_size=1
+        B, _, K = inputs.shape 
+        # past_target = batch_data.past_target_cdf[:, -self.context_length:]
+        start_time = datastamps.reshape(-1)[0]
+        data = [{"start": start_time, "target": inputs[:,:,i].cpu().squeeze()} for i in range(K)]
+        dataset = ListDataset(data, freq='1h')
+
+        forecasts = self.predictor.predict(dataset, num_samples=num_samples)
+        samples = [fs.samples for fs in forecasts]
+        forecasts = np.array(samples).transpose(1, 2, 0)
+
+        prob_forecast = forecasts[np.newaxis, :, :]
+        prob_forecast = torch.tensor(prob_forecast) # shape: b s l k
+        
+        return prob_forecast
diff --git a/probts/model/forecaster/prob_forecaster/moirai.py b/probts/model/forecaster/prob_forecaster/moirai.py
new file mode 100644
index 0000000..dee60b8
--- /dev/null
+++ b/probts/model/forecaster/prob_forecaster/moirai.py
@@ -0,0 +1,62 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from uni2ts
+# - Source: https://github.com/SalesforceAIResearch/uni2ts
+# - Paper: Unified Training of Universal Time Series Forecasting Transformers
+# - License: Apache License 2.0
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+from typing import Union
+from probts.model.forecaster import Forecaster
+from einops import rearrange, repeat 
+from probts.model.nn.layers.Moirai_backbone import MoiraiBackbone
+from uni2ts.model.moirai.module import MoiraiModule
+
+
+class Moirai(Forecaster):
+    def __init__(
+        self,
+        variate_mode: str = 'M',
+        patch_size: Union[str, int] = 'auto',
+        model_size: str = 'base',
+        scaling: bool = True,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.variate_mode = variate_mode
+        self.patch_size = patch_size
+        
+        # Load pretrained model
+        self.no_training = True
+        self.moirai = MoiraiBackbone(
+            module=MoiraiModule.from_pretrained(f"Salesforce/moirai-1.0-R-{model_size}"),
+            prediction_length=self.prediction_length,
+            context_length=self.context_length,
+            patch_size=self.patch_size,
+            target_dim=self.target_dim if self.variate_mode == 'M' else 1,
+            scaling=scaling
+        )
+
+    def forecast(self, batch_data, num_samples=None):
+        if self.variate_mode == 'M':
+            forecasts = self.moirai(
+                past_target=batch_data.past_target_cdf,
+                past_observed_target=batch_data.past_observed_values,
+                past_is_pad=batch_data.past_is_pad,
+                num_samples=num_samples
+            )
+        elif self.variate_mode == 'S':
+            B, L, K = batch_data.past_target_cdf.shape
+            forecasts = self.moirai(
+                past_target=rearrange(batch_data.past_target_cdf, 'b l k -> (b k) l').unsqueeze(-1),
+                past_observed_target=rearrange(batch_data.past_observed_values, 'b l k -> (b k) l').unsqueeze(-1),
+                past_is_pad=repeat(batch_data.past_is_pad, 'b l -> (b k) l', k=K),
+                num_samples=num_samples
+            )
+            forecasts = forecasts.squeeze(-1)
+            forecasts = rearrange(forecasts, '(b k) n l -> b n l k', b=B, k=K)
+        else:
+            raise ValueError(f"Unknown variate mode: {self.variate_mode}")
+        return forecasts
diff --git a/probts/model/forecaster/prob_forecaster/timegrad.py b/probts/model/forecaster/prob_forecaster/timegrad.py
new file mode 100644
index 0000000..a0cd8ea
--- /dev/null
+++ b/probts/model/forecaster/prob_forecaster/timegrad.py
@@ -0,0 +1,112 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PyTorch-TS
+# - Source: https://github.com/zalandoresearch/pytorch-ts
+# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
+# - License: MIT, Apache-2.0 license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+
+from probts.data import ProbTSBatchData
+from probts.utils import repeat
+from probts.model.forecaster import Forecaster
+from probts.model.nn import GaussianDiffusion
+
+
+class TimeGrad(Forecaster):
+    def __init__(
+        self,
+        enc_num_layers: int = 2,
+        enc_hidden_size: int = 40,
+        enc_dropout: float = 0.1,
+        conditional_length: int = 100,
+        beta_end: float = 0.1,
+        diff_steps: int = 100,
+        loss_type: str = "l2",
+        beta_schedule: str = "linear",
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.autoregressive = True
+        
+        self.encoder = nn.GRU(
+            input_size=self.input_size,
+            hidden_size=enc_hidden_size,
+            num_layers=enc_num_layers,
+            dropout=enc_dropout,
+            batch_first=True
+        )
+        self.prob_model = GaussianDiffusion(
+            target_dim=self.target_dim,
+            f_hidden_size=enc_hidden_size,
+            conditional_length=conditional_length,
+            beta_end=beta_end,
+            diff_steps=diff_steps,
+            loss_type=loss_type,
+            beta_schedule=beta_schedule
+        )
+
+    def loss(self, batch_data):
+        if self.use_scaling:
+            self.get_scale(batch_data)
+            self.prob_model.scale = self.scaler.scale
+        
+        inputs = self.get_inputs(batch_data, 'all')
+        enc_outs, states = self.encoder(inputs)
+        enc_outs = enc_outs[:, -self.prediction_length-1:-1, ...]
+        
+        dist_args = self.prob_model.dist_args(enc_outs)
+        loss = self.prob_model.loss(batch_data.future_target_cdf, dist_args).unsqueeze(-1)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        if self.use_scaling:
+            self.get_scale(batch_data)
+        
+        states = self.encode(batch_data)
+        
+        repeated_target_dimension_indicator = repeat(batch_data.target_dimension_indicator, num_samples)
+        repeated_past_target_cdf = repeat(batch_data.past_target_cdf, num_samples)
+        repeated_future_time_feat = repeat(batch_data.future_time_feat, num_samples)
+        repeated_states = repeat(states, num_samples, dim=1)
+        if self.use_scaling:
+            repeated_scale = repeat(self.scaler.scale, num_samples)
+            self.scaler.scale = repeated_scale
+            self.prob_model.scale = repeated_scale
+
+        future_samples = []
+        for k in range(self.prediction_length):
+            repeated_batch_data = ProbTSBatchData({
+                'target_dimension_indicator': repeated_target_dimension_indicator,
+                'past_target_cdf': repeated_past_target_cdf,
+                'future_time_feat': repeated_future_time_feat[:, k:k+1, ...]
+            }, device=batch_data.device)
+
+            enc_outs, repeated_states = self.decode(repeated_batch_data, repeated_states)
+            # Sample
+            dist_args = self.prob_model.dist_args(enc_outs)
+            new_samples = self.prob_model.sample(cond=dist_args)
+            future_samples.append(new_samples)
+
+            repeated_past_target_cdf = torch.cat(
+                (repeated_past_target_cdf, new_samples), dim=1
+            )
+
+        forecasts = torch.cat(future_samples, dim=1).reshape(
+            -1, num_samples, self.prediction_length, self.target_dim)
+        return forecasts
+
+    def encode(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        outputs, states = self.encoder(inputs)
+        return states
+
+    def decode(self, batch_data, states=None):
+        inputs = self.get_inputs(batch_data, 'decode')
+        outputs, states = self.encoder(inputs, states)
+        return outputs, states
diff --git a/probts/model/forecaster/prob_forecaster/trans_maf.py b/probts/model/forecaster/prob_forecaster/trans_maf.py
new file mode 100644
index 0000000..662175b
--- /dev/null
+++ b/probts/model/forecaster/prob_forecaster/trans_maf.py
@@ -0,0 +1,138 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PyTorch-TS
+# - Source: https://github.com/zalandoresearch/pytorch-ts
+# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
+# - License: MIT, Apache-2.0 license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+
+from probts.data import ProbTSBatchData
+from probts.utils import repeat
+from probts.model.forecaster import Forecaster
+from probts.model.nn import MAF
+
+
+class Trans_MAF(Forecaster):
+    def __init__(
+        self,
+        enc_hidden_size: int = 32,
+        enc_num_heads: int = 8,
+        enc_num_encoder_layers: int = 3,
+        enc_num_decoder_layers: int = 3,
+        enc_dim_feedforward_scale: int = 4,
+        enc_dropout: float = 0.1,
+        enc_activation: str = 'gelu',
+        n_blocks: int = 4,
+        hidden_size: int = 100,
+        n_hidden: int = 2,
+        conditional_length: int = 200,
+        dequantize: bool = False,
+        batch_norm: bool = True,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.autoregressive = True
+
+        self.enc_linear = nn.Linear(self.input_size, enc_hidden_size)
+        self.dec_linear = nn.Linear(self.input_size, enc_hidden_size)
+        self.model = nn.Transformer(
+            d_model=enc_hidden_size,
+            nhead=enc_num_heads,
+            num_encoder_layers=enc_num_encoder_layers,
+            num_decoder_layers=enc_num_decoder_layers,
+            dim_feedforward=enc_dim_feedforward_scale * enc_hidden_size,
+            dropout=enc_dropout,
+            activation=enc_activation
+        )
+
+        self.register_buffer(
+            "tgt_mask",
+            self.model.generate_square_subsequent_mask(self.prediction_length),
+        )
+        
+        self.prob_model = MAF(
+            n_blocks=n_blocks,
+            target_dim=self.target_dim,
+            hidden_size=hidden_size,
+            n_hidden=n_hidden,
+            f_hidden_size=enc_hidden_size,
+            conditional_length=conditional_length,
+            dequantize=dequantize,
+            batch_norm=batch_norm
+        )
+
+    def loss(self, batch_data):
+        if self.use_scaling:
+            self.get_scale(batch_data)
+            self.prob_model.scale = self.scaler.scale
+        
+        inputs = self.get_inputs(batch_data, 'all') # [B L D]
+
+        enc_inputs = inputs[:, :self.context_length, ...]
+        enc_inputs = self.enc_linear(enc_inputs).permute(1, 0, 2)
+        enc_outputs = self.model.encoder(enc_inputs) # [L_in B H]
+
+        dec_inputs = inputs[:, -self.prediction_length-1:-1, ...]
+        dec_inputs = self.dec_linear(dec_inputs).permute(1, 0, 2)
+        dec_outputs = self.model.decoder(
+            dec_inputs, enc_outputs, tgt_mask=self.tgt_mask)
+        dec_outputs = dec_outputs.permute(1, 0, 2)  # [L_out B D]
+        
+        dist_args = self.prob_model.dist_args(dec_outputs)
+        loss = self.prob_model.loss(batch_data.future_target_cdf, dist_args).unsqueeze(-1)
+        loss = self.get_weighted_loss(batch_data, loss)
+        return loss.mean()
+
+    def forecast(self, batch_data, num_samples=None):
+        if self.use_scaling:
+            self.get_scale(batch_data)
+        
+        states = self.encode(batch_data)
+        
+        repeated_target_dimension_indicator = repeat(batch_data.target_dimension_indicator, num_samples)
+        repeated_past_target_cdf = repeat(batch_data.past_target_cdf, num_samples)
+        repeated_future_time_feat = repeat(batch_data.future_time_feat, num_samples)
+        repeated_states = repeat(states, num_samples, dim=1)
+        if self.use_scaling:
+            repeated_scale = repeat(self.scaler.scale, num_samples)
+            self.scaler.scale = repeated_scale
+            self.prob_model.scale = repeated_scale
+
+        future_samples = []
+        for k in range(self.prediction_length):
+            repeated_batch_data = ProbTSBatchData({
+                'target_dimension_indicator': repeated_target_dimension_indicator,
+                'past_target_cdf': repeated_past_target_cdf,
+                'future_time_feat': repeated_future_time_feat[:, k:k+1, ...]
+            }, device=batch_data.device)
+
+            enc_outs, repeated_states = self.decode(repeated_batch_data, repeated_states)
+            # Sample
+            dist_args = self.prob_model.dist_args(enc_outs)
+            new_samples = self.prob_model.sample(cond=dist_args)
+            future_samples.append(new_samples)
+
+            repeated_past_target_cdf = torch.cat(
+                (repeated_past_target_cdf, new_samples), dim=1
+            )
+
+        forecasts = torch.cat(future_samples, dim=1).reshape(
+            -1, num_samples, self.prediction_length, self.target_dim)
+        return forecasts
+
+    def encode(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'encode')
+        inputs = self.enc_linear(inputs).permute(1, 0, 2)
+        states = self.model.encoder(inputs)
+        return states
+
+    def decode(self, batch_data, states=None):
+        inputs = self.get_inputs(batch_data, 'decode')
+        inputs = self.dec_linear(inputs).permute(1, 0, 2)
+        outputs = self.model.decoder(inputs, states, tgt_mask=None)
+        return outputs.permute(1, 0, 2), states
diff --git a/probts/model/forecaster/prob_forecaster/tsdiff.py b/probts/model/forecaster/prob_forecaster/tsdiff.py
new file mode 100644
index 0000000..887488d
--- /dev/null
+++ b/probts/model/forecaster/prob_forecaster/tsdiff.py
@@ -0,0 +1,242 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from TSDiff
+# - Source: https://github.com/amazon-science/unconditional-time-series-diffusion
+# - Paper: Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
+# - License: Apache-2.0
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+import torch
+import torch.nn as nn
+from probts.model.nn.layers.s4_backbones import BackboneModel
+from einops import rearrange
+from probts.utils import repeat
+import torch.nn.functional as F
+from probts.utils import extract
+from probts.model.forecaster import Forecaster
+import sys
+import numpy as np
+
+def linear_beta_schedule(timesteps):
+    beta_start = 0.0001
+    beta_end = 0.1
+    return torch.linspace(beta_start, beta_end, timesteps)
+
+
+class TSDiffCond(Forecaster):
+    def __init__(
+        self,
+        hidden_dim: int,
+        step_emb: int,
+        timesteps: int,
+        num_residual_blocks: int,
+        dropout: float = 0,
+        # use_features: bool = False,
+        init_skip=True,
+        noise_observed=False, # reconstruct past
+        mode="diag",
+        measure="diag",
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        backbone_parameters = {
+            "input_dim": self.target_dim,
+            "hidden_dim": hidden_dim,
+            "output_dim": self.target_dim,
+            "step_emb": step_emb,
+            "num_residual_blocks": num_residual_blocks,
+            "residual_block": "s4",
+            "mode": mode,
+            'measure': measure,
+        }
+        # self.use_features=use_features
+        self.timesteps = timesteps
+        self.betas = linear_beta_schedule(timesteps)
+        self.sqrt_one_minus_beta = torch.sqrt(1.0 - self.betas)
+        self.alphas = 1 - self.betas
+        self.alphas_cumprod = torch.cumprod(self.alphas, axis=0)
+        self.alphas_cumprod_prev = F.pad(
+            self.alphas_cumprod[:-1], (1, 0), value=1.0
+        )
+        self.sqrt_recip_alphas = torch.sqrt(1.0 / self.alphas)
+        self.sqrt_alphas_cumprod = torch.sqrt(self.alphas_cumprod)
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(
+            1.0 - self.alphas_cumprod
+        )
+        self.posterior_variance = (
+            self.betas
+            * (1.0 - self.alphas_cumprod_prev)
+            / (1.0 - self.alphas_cumprod)
+        )
+        self.backbone = BackboneModel(
+            **backbone_parameters,
+            num_features=self.target_dim,
+            init_skip=init_skip,
+            dropout=dropout,
+        )
+        self.noise_observed = noise_observed
+
+    def _extract_features(self, batch_data):
+        inputs = self.get_inputs(batch_data, 'all')
+        x = inputs[:,:, :self.target_dim]
+        features = inputs.clone()
+        
+        if self.use_time_feat:
+            features[:,self.context_length:, :self.target_dim] = 0
+        else:
+            features = features[:,:, :self.target_dim]
+            features[:,self.context_length:] = 0
+        
+        observation_mask = torch.zeros_like(x, device=x.device)
+        observation_mask[:,:self.context_length] = 1
+        
+        return x, features, observation_mask
+
+    def q_sample(self, x_start, t, noise=None):
+        device = next(self.backbone.parameters()).device
+        if noise is None:
+            noise = torch.randn_like(x_start, device=device)
+        sqrt_alphas_cumprod_t = extract(
+            self.sqrt_alphas_cumprod, t, x_start.shape
+        )
+        sqrt_one_minus_alphas_cumprod_t = extract(
+            self.sqrt_one_minus_alphas_cumprod, t, x_start.shape
+        )
+
+        return (
+            sqrt_alphas_cumprod_t * x_start
+            + sqrt_one_minus_alphas_cumprod_t * noise
+        )
+
+    def p_losses(
+        self,
+        x_start,
+        t,
+        features=None,
+        noise=None,
+        loss_type="l2",
+        reduction="mean",
+    ):
+        device = next(self.backbone.parameters()).device
+        if noise is None:
+            noise = torch.randn_like(x_start, device=device)
+
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        predicted_noise = self.backbone(x_noisy, t, features)
+
+        if loss_type == "l1":
+            loss = F.l1_loss(noise, predicted_noise, reduction=reduction)
+        elif loss_type == "l2":
+            loss = F.mse_loss(noise, predicted_noise, reduction=reduction)
+        elif loss_type == "huber":
+            loss = F.smooth_l1_loss(
+                noise, predicted_noise, reduction=reduction
+            )
+        else:
+            raise NotImplementedError()
+
+        return loss, x_noisy, predicted_noise
+
+    @torch.no_grad()
+    def p_sample(self, x, t, t_index, features=None):
+        betas_t = extract(self.betas, t, x.shape)
+        sqrt_one_minus_alphas_cumprod_t = extract(
+            self.sqrt_one_minus_alphas_cumprod, t, x.shape
+        )
+        sqrt_recip_alphas_t = extract(self.sqrt_recip_alphas, t, x.shape)
+
+
+        predicted_noise = self.backbone(x, t, features)
+
+        model_mean = sqrt_recip_alphas_t * (
+            x - betas_t * predicted_noise / sqrt_one_minus_alphas_cumprod_t
+        )
+
+        if t_index == 0:
+            return model_mean
+        else:
+            posterior_variance_t = extract(self.posterior_variance, t, x.shape)
+            noise = torch.randn_like(x)
+            return model_mean + torch.sqrt(posterior_variance_t) * noise
+
+    def step(self, x, t, features, loss_mask):
+        noise = torch.randn_like(x)
+        if not self.noise_observed:
+            noise = (1 - loss_mask) * x + noise * loss_mask
+
+        num_eval = loss_mask.sum()
+        sq_err, _, _ = self.p_losses(
+            x,
+            t,
+            features,
+            loss_type="l2",
+            reduction="none",
+            noise=noise,
+        )
+
+        if self.noise_observed:
+            elbo_loss = sq_err.mean()
+        else:
+            sq_err = sq_err * loss_mask
+            elbo_loss = sq_err.sum() / (num_eval if num_eval else 1)
+        return elbo_loss
+
+
+
+    def loss(self, batch_data):
+        # [b l k 1], [b l k 2]
+        x, features, observation_mask = self._extract_features(batch_data)
+        loss_mask = 1 - observation_mask
+
+        t = torch.randint(
+            0, self.timesteps, [x.shape[0]], device=x.device
+        ).long()
+        
+        loss = self.step(x, t, features, loss_mask)
+
+        if torch.isnan(loss):
+            print("Loss is NaN, exiting.")
+            sys.exit(1)
+        return loss
+
+    def forecast(self, batch_data, num_samples):
+        observation, features, observation_mask = self._extract_features(batch_data)
+
+        observation = observation.to(observation.device)
+
+        pred = self.sample(
+            observation=observation,
+            observation_mask=observation_mask,
+            n_samples=num_samples,
+            features=features,
+        )  
+
+        return pred[:,:,-self.prediction_length:,:]
+
+    @torch.no_grad()
+    def sample(self, observation, observation_mask, n_samples, features=None):
+
+        repeated_observation = repeat(observation, n_samples)
+        repeated_observation_mask = repeat(observation_mask, n_samples)
+        repeated_features = repeat(features, n_samples)
+        
+        batch_size, length, ch = repeated_observation.shape
+        seq = torch.randn_like(repeated_observation)
+
+        for i in reversed(range(0, self.timesteps)):
+            if not self.noise_observed:
+                seq = repeated_observation_mask * repeated_observation + seq * (1 - repeated_observation_mask)
+
+            seq = self.p_sample(
+                seq,
+                torch.full((batch_size,), i, device=repeated_observation.device, dtype=torch.long),
+                i,
+                repeated_features,
+            )
+
+        seq = seq.reshape(-1, n_samples, length, ch)
+        return seq 
diff --git a/probts/model/nn/MAF.py b/probts/model/nn/MAF.py
new file mode 100644
index 0000000..b767605
--- /dev/null
+++ b/probts/model/nn/MAF.py
@@ -0,0 +1,197 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PyTorch-TS
+# - Source: https://github.com/zalandoresearch/pytorch-ts
+# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
+# - License: MIT, Apache-2.0 license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributions import Normal
+from .flow_model import FlowModel, BatchNorm, FlowSequential
+
+
+def create_masks(
+    input_size, hidden_size, n_hidden, input_order="sequential", input_degrees=None
+):
+    # MADE paper sec 4:
+    # degrees of connections between layers -- ensure at most in_degree - 1 connections
+    degrees = []
+
+    # set input degrees to what is provided in args (the flipped order of the previous layer in a stack of mades);
+    # else init input degrees based on strategy in input_order (sequential or random)
+    if input_order == "sequential":
+        degrees += (
+            [torch.arange(input_size)] if input_degrees is None else [input_degrees]
+        )
+        for _ in range(n_hidden + 1):
+            degrees += [torch.arange(hidden_size) % (input_size - 1)]
+        degrees += (
+            [torch.arange(input_size) % input_size - 1]
+            if input_degrees is None
+            else [input_degrees % input_size - 1]
+        )
+
+    elif input_order == "random":
+        degrees += (
+            [torch.randperm(input_size)] if input_degrees is None else [input_degrees]
+        )
+        for _ in range(n_hidden + 1):
+            min_prev_degree = min(degrees[-1].min().item(), input_size - 1)
+            degrees += [torch.randint(min_prev_degree, input_size, (hidden_size,))]
+        min_prev_degree = min(degrees[-1].min().item(), input_size - 1)
+        degrees += (
+            [torch.randint(min_prev_degree, input_size, (input_size,)) - 1]
+            if input_degrees is None
+            else [input_degrees - 1]
+        )
+
+    # construct masks
+    masks = []
+    for (d0, d1) in zip(degrees[:-1], degrees[1:]):
+        masks += [(d1.unsqueeze(-1) >= d0.unsqueeze(0)).float()]
+
+    return masks, degrees[0]
+
+
+class MaskedLinear(nn.Linear):
+    """ MADE building block layer """
+
+    def __init__(self, input_size, n_outputs, mask, cond_label_size=None):
+        super().__init__(input_size, n_outputs)
+
+        self.register_buffer("mask", mask)
+
+        self.cond_label_size = cond_label_size
+        if cond_label_size is not None:
+            self.cond_weight = nn.Parameter(
+                torch.rand(n_outputs, cond_label_size) / math.sqrt(cond_label_size)
+            )
+
+    def forward(self, x, y=None):
+        out = F.linear(x, self.weight * self.mask, self.bias)
+        if y is not None:
+            out = out + F.linear(y, self.cond_weight)
+        return out
+
+
+class MADE(nn.Module):
+    def __init__(
+        self,
+        input_size,
+        hidden_size,
+        n_hidden,
+        cond_label_size=None,
+        activation="ReLU",
+        input_order="sequential",
+        input_degrees=None,
+    ):
+        """
+        Args:
+            input_size -- scalar; dim of inputs
+            hidden_size -- scalar; dim of hidden layers
+            n_hidden -- scalar; number of hidden layers
+            activation -- str; activation function to use
+            input_order -- str or tensor; variable order for creating the autoregressive masks (sequential|random)
+                            or the order flipped from the previous layer in a stack of MADEs
+            conditional -- bool; whether model is conditional
+        """
+        super().__init__()
+        # base distribution for calculation of log prob under the model
+        self.register_buffer("base_dist_mean", torch.zeros(input_size))
+        self.register_buffer("base_dist_var", torch.ones(input_size))
+
+        # create masks
+        masks, self.input_degrees = create_masks(
+            input_size, hidden_size, n_hidden, input_order, input_degrees
+        )
+
+        # setup activation
+        if activation == "ReLU":
+            activation_fn = nn.ReLU()
+        elif activation == "Tanh":
+            activation_fn = nn.Tanh()
+        else:
+            raise ValueError("Check activation function.")
+
+        # construct model
+        self.net_input = MaskedLinear(
+            input_size, hidden_size, masks[0], cond_label_size
+        )
+        self.net = []
+        for m in masks[1:-1]:
+            self.net += [activation_fn, MaskedLinear(hidden_size, hidden_size, m)]
+        self.net += [
+            activation_fn,
+            MaskedLinear(hidden_size, 2 * input_size, masks[-1].repeat(2, 1)),
+        ]
+        self.net = nn.Sequential(*self.net)
+
+    @property
+    def base_dist(self):
+        return Normal(self.base_dist_mean, self.base_dist_var)
+
+    def forward(self, x, y=None):
+        # MAF eq 4 -- return mean and log std
+        m, loga = self.net(self.net_input(x, y)).chunk(chunks=2, dim=-1)
+        u = (x - m) * torch.exp(-loga)
+        # MAF eq 5
+        log_abs_det_jacobian = -loga
+        return u, log_abs_det_jacobian
+
+    def inverse(self, u, y=None, sum_log_abs_det_jacobians=None):
+        # MAF eq 3
+        # D = u.shape[-1]
+        x = torch.zeros_like(u)
+        # run through reverse model
+        for i in self.input_degrees:
+            m, loga = self.net(self.net_input(x, y)).chunk(chunks=2, dim=-1)
+            x[..., i] = u[..., i] * torch.exp(loga[..., i]) + m[..., i]
+        log_abs_det_jacobian = loga
+        return x, log_abs_det_jacobian
+
+    def log_prob(self, x, y=None):
+        u, log_abs_det_jacobian = self.forward(x, y)
+        return torch.sum(self.base_dist.log_prob(u) + log_abs_det_jacobian, dim=-1)
+
+
+class MAF(FlowModel):
+    def __init__(
+        self,
+        n_blocks,
+        target_dim,
+        hidden_size,
+        n_hidden,
+        f_hidden_size,
+        conditional_length,
+        dequantize,
+        activation="ReLU",
+        input_order="sequential",
+        batch_norm=True,
+    ):
+        super().__init__(target_dim, f_hidden_size, conditional_length, dequantize)
+
+        # construct model
+        modules = []
+        self.input_degrees = None
+        for i in range(n_blocks):
+            modules += [
+                MADE(
+                    target_dim,
+                    hidden_size,
+                    n_hidden,
+                    conditional_length,
+                    activation,
+                    input_order,
+                    self.input_degrees,
+                )
+            ]
+            self.input_degrees = modules[-1].input_degrees.flip(0)
+            modules += batch_norm * [BatchNorm(target_dim)]
+
+        self.net = FlowSequential(*modules)
\ No newline at end of file
diff --git a/probts/model/nn/RealNVP.py b/probts/model/nn/RealNVP.py
new file mode 100644
index 0000000..82c7f17
--- /dev/null
+++ b/probts/model/nn/RealNVP.py
@@ -0,0 +1,115 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PyTorch-TS
+# - Source: https://github.com/zalandoresearch/pytorch-ts
+# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
+# - License: MIT, Apache-2.0 license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import copy
+import torch
+import torch.nn as nn
+from .flow_model import FlowModel, BatchNorm, FlowSequential
+
+
+class LinearMaskedCoupling(nn.Module):
+    """ Modified RealNVP Coupling Layers per the MAF paper """
+
+    def __init__(self, input_size, hidden_size, n_hidden, mask, cond_label_size=None):
+        super().__init__()
+
+        self.register_buffer("mask", mask)
+
+        # scale function
+        s_net = [
+            nn.Linear(
+                input_size + (cond_label_size if cond_label_size is not None else 0),
+                hidden_size,
+            )
+        ]
+        for _ in range(n_hidden):
+            s_net += [nn.Tanh(), nn.Linear(hidden_size, hidden_size)]
+        s_net += [nn.Tanh(), nn.Linear(hidden_size, input_size)]
+        self.s_net = nn.Sequential(*s_net)
+
+        # translation function
+        self.t_net = copy.deepcopy(self.s_net)
+        # replace Tanh with ReLU's per MAF paper
+        for i in range(len(self.t_net)):
+            if not isinstance(self.t_net[i], nn.Linear):
+                self.t_net[i] = nn.ReLU()
+
+    def forward(self, x, y=None):
+        # apply mask
+        mx = x * self.mask
+
+        # run through model
+        s = self.s_net(mx if y is None else torch.cat([y, mx], dim=-1))
+        t = self.t_net(mx if y is None else torch.cat([y, mx], dim=-1)) * (
+            1 - self.mask
+        )
+
+        # cf RealNVP eq 8 where u corresponds to x (here we're modeling u)
+        log_s = torch.tanh(s) * (1 - self.mask)
+        u = x * torch.exp(log_s) + t
+        # u = (x - t) * torch.exp(log_s)
+        # u = mx + (1 - self.mask) * (x - t) * torch.exp(-s)
+
+        # log det du/dx; cf RealNVP 8 and 6; note, sum over input_size done at model log_prob
+        # log_abs_det_jacobian = -(1 - self.mask) * s
+        # log_abs_det_jacobian = -log_s #.sum(-1, keepdim=True)
+        log_abs_det_jacobian = log_s
+
+        return u, log_abs_det_jacobian
+
+    def inverse(self, u, y=None):
+        # apply mask
+        mu = u * self.mask
+
+        # run through model
+        s = self.s_net(mu if y is None else torch.cat([y, mu], dim=-1))
+        t = self.t_net(mu if y is None else torch.cat([y, mu], dim=-1)) * (
+            1 - self.mask
+        )
+
+        log_s = torch.tanh(s) * (1 - self.mask)
+        x = (u - t) * torch.exp(-log_s)
+        # x = u * torch.exp(log_s) + t
+        # x = mu + (1 - self.mask) * (u * s.exp() + t)  # cf RealNVP eq 7
+
+        # log_abs_det_jacobian = (1 - self.mask) * s  # log det dx/du
+        # log_abs_det_jacobian = log_s #.sum(-1, keepdim=True)
+        log_abs_det_jacobian = -log_s
+
+        return x, log_abs_det_jacobian
+
+
+class RealNVP(FlowModel):
+    def __init__(
+        self,
+        n_blocks,
+        target_dim,
+        hidden_size,
+        n_hidden,
+        f_hidden_size,
+        conditional_length,
+        dequantize,
+        batch_norm=True
+    ):
+        super().__init__(target_dim, f_hidden_size, conditional_length, dequantize)
+
+        # construct model
+        modules = []
+        mask = torch.arange(target_dim).float() % 2
+        for i in range(n_blocks):
+            modules += [
+                LinearMaskedCoupling(
+                    target_dim, hidden_size, n_hidden, mask, conditional_length
+                )
+            ]
+            mask = 1 - mask
+            modules += batch_norm * [BatchNorm(target_dim)]
+
+        self.net = FlowSequential(*modules)
\ No newline at end of file
diff --git a/probts/model/nn/__init__.py b/probts/model/nn/__init__.py
new file mode 100644
index 0000000..5b90d06
--- /dev/null
+++ b/probts/model/nn/__init__.py
@@ -0,0 +1,3 @@
+from .RealNVP import RealNVP
+from .MAF import MAF
+from .gaussian_diffusion import GaussianDiffusion
\ No newline at end of file
diff --git a/probts/model/nn/flow_model.py b/probts/model/nn/flow_model.py
new file mode 100644
index 0000000..6469a26
--- /dev/null
+++ b/probts/model/nn/flow_model.py
@@ -0,0 +1,149 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PyTorch-TS
+# - Source: https://github.com/zalandoresearch/pytorch-ts
+# - Paper: Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
+# - License: MIT, Apache-2.0 license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+from torch.distributions import Normal
+
+class FlowModel(nn.Module):
+    def __init__(self, target_dim, f_hidden_size, conditional_length, dequantize):
+        super().__init__()
+        self.__scale = None
+        self.net = None
+        self.dequantize = dequantize
+
+        self.dist_args = nn.Linear(
+            in_features=f_hidden_size, out_features=conditional_length
+        )
+
+        # base distribution for calculation of log prob under the model
+        self.register_buffer("base_dist_mean", torch.zeros(target_dim))
+        self.register_buffer("base_dist_var", torch.ones(target_dim))
+
+    @property
+    def base_dist(self):
+        return Normal(self.base_dist_mean, self.base_dist_var)
+
+    @property
+    def scale(self):
+        return self.__scale
+    
+    @scale.setter
+    def scale(self, scale):
+        self.__scale = scale
+
+    def forward(self, x, cond):
+        if self.scale is not None:
+            x /= self.scale
+        u, log_abs_det_jacobian = self.net(x, cond)
+        return u, log_abs_det_jacobian
+
+    def inverse(self, u, cond):
+        x, log_abs_det_jacobian = self.net.inverse(u, cond)
+        if self.scale is not None:
+            x *= self.scale
+            log_abs_det_jacobian += torch.log(torch.abs(self.scale))
+        return x, log_abs_det_jacobian
+
+    def log_prob(self, x, cond):
+        if self.dequantize:
+            x += torch.rand_like(x)
+        u, sum_log_abs_det_jacobians = self.forward(x, cond)
+        return torch.sum(self.base_dist.log_prob(u) + sum_log_abs_det_jacobians, dim=-1)
+
+    def loss(self, x, cond):
+        return -self.log_prob(x, cond)
+
+    def sample(self, sample_shape=torch.Size(), cond=None):
+        if cond is not None:
+            shape = cond.shape[:-1]
+        else:
+            shape = sample_shape
+
+        u = self.base_dist.sample(shape)
+        sample, _ = self.inverse(u, cond)
+        return sample
+
+
+class BatchNorm(nn.Module):
+    """ Flow Model BatchNorm layer """
+
+    def __init__(self, input_size, momentum=0.9, eps=1e-5):
+        super().__init__()
+        self.momentum = momentum
+        self.eps = eps
+
+        self.log_gamma = nn.Parameter(torch.zeros(input_size))
+        self.beta = nn.Parameter(torch.zeros(input_size))
+
+        self.register_buffer("running_mean", torch.zeros(input_size))
+        self.register_buffer("running_var", torch.ones(input_size))
+
+    def forward(self, x, cond_y=None):
+        if self.training:
+            self.batch_mean = x.view(-1, x.shape[-1]).mean(0)
+            # note MAF paper uses biased variance estimate; ie x.var(0, unbiased=False)
+            self.batch_var = x.view(-1, x.shape[-1]).var(0)
+
+            # update running mean
+            self.running_mean.mul_(self.momentum).add_(
+                self.batch_mean.data * (1 - self.momentum)
+            )
+            self.running_var.mul_(self.momentum).add_(
+                self.batch_var.data * (1 - self.momentum)
+            )
+
+            mean = self.batch_mean
+            var = self.batch_var
+        else:
+            mean = self.running_mean
+            var = self.running_var
+
+        # compute normalized input (cf original batch norm paper algo 1)
+        x_hat = (x - mean) / torch.sqrt(var + self.eps)
+        y = self.log_gamma.exp() * x_hat + self.beta
+
+        # compute log_abs_det_jacobian (cf RealNVP paper)
+        log_abs_det_jacobian = self.log_gamma - 0.5 * torch.log(var + self.eps)
+        
+        return y, log_abs_det_jacobian.expand_as(x)
+
+    def inverse(self, y, cond_y=None):
+        if self.training:
+            mean = self.batch_mean
+            var = self.batch_var
+        else:
+            mean = self.running_mean
+            var = self.running_var
+
+        x_hat = (y - self.beta) * torch.exp(-self.log_gamma)
+        x = x_hat * torch.sqrt(var + self.eps) + mean
+
+        log_abs_det_jacobian = 0.5 * torch.log(var + self.eps) - self.log_gamma
+
+        return x, log_abs_det_jacobian.expand_as(x)
+
+
+class FlowSequential(nn.Sequential):
+    """ Container for layers of a normalizing flow """
+
+    def forward(self, x, y):
+        sum_log_abs_det_jacobians = 0
+        for module in self:
+            x, log_abs_det_jacobian = module(x, y)
+            sum_log_abs_det_jacobians += log_abs_det_jacobian
+        return x, sum_log_abs_det_jacobians
+
+    def inverse(self, u, y):
+        sum_log_abs_det_jacobians = 0
+        for module in reversed(self):
+            u, log_abs_det_jacobian = module.inverse(u, y)
+            sum_log_abs_det_jacobians += log_abs_det_jacobian
+        return u, sum_log_abs_det_jacobians
\ No newline at end of file
diff --git a/probts/model/nn/gaussian_diffusion.py b/probts/model/nn/gaussian_diffusion.py
new file mode 100644
index 0000000..e5f377f
--- /dev/null
+++ b/probts/model/nn/gaussian_diffusion.py
@@ -0,0 +1,424 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PyTorch-TS
+# - Source: https://github.com/zalandoresearch/pytorch-ts
+# - Paper: Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
+# - License: MIT, Apache-2.0 license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import math
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from torch.distributions import Normal
+from probts.model.forecaster import Forecaster
+from .layers import DiffusionEmbedding
+from functools import partial
+from inspect import isfunction
+
+
+def default(val, d):
+    if val is not None:
+        return val
+    return d() if isfunction(d) else d
+
+
+def extract(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def noise_like(shape, device, repeat=False):
+    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(
+        shape[0], *((1,) * (len(shape) - 1))
+    )
+    noise = lambda: torch.randn(shape, device=device)
+    return repeat_noise() if repeat else noise()
+
+
+def cosine_beta_schedule(timesteps, s=0.008):
+    """
+    cosine schedule
+    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
+    """
+    steps = timesteps + 1
+    x = np.linspace(0, timesteps, steps)
+    alphas_cumprod = np.cos(((x / timesteps) + s) / (1 + s) * np.pi * 0.5) ** 2
+    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
+    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
+    return np.clip(betas, 0, 0.999)
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, hidden_size, residual_channels, dilation, target_dim):
+        super().__init__()
+        self.target_dim = target_dim
+        
+        self.diffusion_projection = nn.Linear(hidden_size, residual_channels)
+
+        if self.target_dim > 1:
+            self.dilated_conv = nn.Conv1d(
+                residual_channels,
+                2 * residual_channels,
+                3,
+                padding=dilation,
+                dilation=dilation,
+                padding_mode="circular",
+            )
+            self.conditioner_projection = nn.Conv1d(
+                1, 2 * residual_channels, 1, padding=2, padding_mode="circular"
+            )
+        else:
+            self.dilated_conv = nn.Conv1d(residual_channels,2 * residual_channels,1)
+            self.conditioner_projection = nn.Conv1d(1, 2 * residual_channels, 1)
+
+        self.output_projection = nn.Conv1d(residual_channels, 2 * residual_channels, 1)
+
+        nn.init.kaiming_normal_(self.conditioner_projection.weight)
+        nn.init.kaiming_normal_(self.output_projection.weight)
+
+    def forward(self, x, conditioner, diffusion_step):
+        diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
+        conditioner = self.conditioner_projection(conditioner)
+
+        y = x + diffusion_step
+        y = self.dilated_conv(y) + conditioner
+
+        gate, filter = torch.chunk(y, 2, dim=1)
+        y = torch.sigmoid(gate) * torch.tanh(filter)
+
+        y = self.output_projection(y)
+        y = F.leaky_relu(y, 0.4)
+        residual, skip = torch.chunk(y, 2, dim=1)
+        return (x + residual) / math.sqrt(2.0), skip
+
+
+class CondUpsampler(nn.Module):
+    def __init__(self, cond_length, target_dim):
+        super().__init__()
+        self.target_dim = target_dim
+
+        if self.target_dim > 1:
+            self.linear1 = nn.Linear(cond_length, target_dim // 2)
+            self.linear2 = nn.Linear(target_dim // 2, target_dim)
+        else:
+            self.linear = nn.Linear(cond_length, target_dim)
+
+    def forward(self, x):
+        if self.target_dim > 1:
+            x = self.linear1(x)
+            x = F.leaky_relu(x, 0.4)
+            x = self.linear2(x)
+            x = F.leaky_relu(x, 0.4)
+        else:
+            x = self.linear(x)
+            x = F.leaky_relu(x, 0.4)
+        return x
+
+
+class EpsilonTheta(nn.Module):
+    def __init__(
+        self,
+        target_dim,
+        cond_length,
+        time_emb_dim=16,
+        residual_layers=8,
+        residual_channels=8,
+        dilation_cycle_length=2,
+        residual_hidden=64,
+        padding=2
+    ):
+        super().__init__()
+        if target_dim > 1:
+            self.input_projection = nn.Conv1d(
+                1, residual_channels, 1, padding=padding, padding_mode="circular"
+            )
+            self.skip_projection = nn.Conv1d(residual_channels, residual_channels, 3)
+            self.output_projection = nn.Conv1d(residual_channels, 1, 3)
+        else:
+            # self.input_projection = nn.Identity()
+            self.input_projection = nn.Conv1d(1, residual_channels, 1)
+            self.skip_projection = nn.Conv1d(residual_channels, residual_channels, 1)
+            self.output_projection = nn.Conv1d(residual_channels, 1, 1)
+
+        self.diffusion_embedding = DiffusionEmbedding(
+            time_emb_dim, proj_dim=residual_hidden
+        )
+        self.cond_upsampler = CondUpsampler(
+            target_dim=target_dim, cond_length=cond_length
+        )
+        self.residual_layers = nn.ModuleList(
+            [
+                ResidualBlock(
+                    residual_channels=residual_channels,
+                    dilation=2 ** (i % dilation_cycle_length),
+                    hidden_size=residual_hidden,
+                    target_dim=target_dim,
+                )
+                for i in range(residual_layers)
+            ]
+        )
+
+        nn.init.kaiming_normal_(self.input_projection.weight)
+        nn.init.kaiming_normal_(self.skip_projection.weight)
+        nn.init.zeros_(self.output_projection.weight)
+
+    def forward(self, inputs, time, cond):
+        x = self.input_projection(inputs)
+        x = F.leaky_relu(x, 0.4)
+
+        diffusion_step = self.diffusion_embedding(time)
+        cond_up = self.cond_upsampler(cond)
+        skip = []
+        for layer in self.residual_layers:
+            x, skip_connection = layer(x, cond_up, diffusion_step)
+            skip.append(skip_connection)
+
+        x = torch.sum(torch.stack(skip), dim=0) / math.sqrt(len(self.residual_layers))
+        x = self.skip_projection(x)
+        x = F.leaky_relu(x, 0.4)
+        x = self.output_projection(x)
+        return x
+
+
+class GaussianDiffusion(nn.Module):
+    def __init__(
+        self,
+        target_dim,
+        f_hidden_size,
+        conditional_length,
+        beta_end=0.1,
+        diff_steps=100,
+        loss_type="l2",
+        betas=None,
+        beta_schedule="linear",
+        padding=2,
+        residual_channels=8,
+    ):
+        super().__init__()
+        self.dist_args = nn.Linear(
+            in_features=f_hidden_size, out_features=conditional_length
+        )
+        self.denoise_fn = EpsilonTheta(
+            target_dim=target_dim,
+            cond_length=conditional_length,
+            residual_channels=residual_channels,
+            padding=padding,
+        )
+        self.target_dim = target_dim
+        self.__scale = None
+
+        if betas is not None:
+            betas = (
+                betas.detach().cpu().numpy()
+                if isinstance(betas, torch.Tensor)
+                else betas
+            )
+        else:
+            if beta_schedule == "linear":
+                betas = np.linspace(1e-4, beta_end, diff_steps)
+            elif beta_schedule == "quad":
+                betas = np.linspace(1e-4 ** 0.5, beta_end ** 0.5, diff_steps) ** 2
+            elif beta_schedule == "const":
+                betas = beta_end * np.ones(diff_steps)
+            elif beta_schedule == "jsd":  # 1/T, 1/(T-1), 1/(T-2), ..., 1
+                betas = 1.0 / np.linspace(diff_steps, 1, diff_steps)
+            elif beta_schedule == "sigmoid":
+                betas = np.linspace(-6, 6, diff_steps)
+                betas = (beta_end - 1e-4) / (np.exp(-betas) + 1) + 1e-4
+            elif beta_schedule == "cosine":
+                betas = cosine_beta_schedule(diff_steps)
+            else:
+                raise NotImplementedError(beta_schedule)
+
+        alphas = 1.0 - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
+
+        (timesteps,) = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.loss_type = loss_type
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer("betas", to_torch(betas))
+        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
+        self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer(
+            "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
+        )
+        self.register_buffer(
+            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
+        )
+        self.register_buffer(
+            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
+        )
+        self.register_buffer(
+            "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
+        )
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = (
+            betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
+        )
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer("posterior_variance", to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer(
+            "posterior_log_variance_clipped",
+            to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
+        )
+        self.register_buffer(
+            "posterior_mean_coef1",
+            to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
+        )
+        self.register_buffer(
+            "posterior_mean_coef2",
+            to_torch(
+                (1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
+            ),
+        )
+
+    @property
+    def scale(self):
+        return self.__scale
+
+    @scale.setter
+    def scale(self, scale):
+        self.__scale = scale
+
+    def q_mean_variance(self, x_start, t):
+        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+        variance = extract(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+            extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+            extract(self.posterior_mean_coef1, t, x_t.shape) * x_start
+            + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract(
+            self.posterior_log_variance_clipped, t, x_t.shape
+        )
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, cond, t, clip_denoised: bool):
+        x_recon = self.predict_start_from_noise(
+            x, t=t, noise=self.denoise_fn(x, t, cond=cond)
+        )
+
+        if clip_denoised:
+            x_recon.clamp_(-1.0, 1.0)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
+            x_start=x_recon, x_t=x, t=t
+        )
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, cond, t, clip_denoised=False, repeat_noise=False):
+        b, *_, device = *x.shape, x.device
+        model_mean, _, model_log_variance = self.p_mean_variance(
+            x=x, cond=cond, t=t, clip_denoised=clip_denoised
+        )
+        noise = noise_like(x.shape, device, repeat_noise)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def p_sample_loop(self, shape, cond):
+        device = self.betas.device
+
+        b = shape[0]
+        img = torch.randn(shape, device=device)
+
+        for i in reversed(range(0, self.num_timesteps)):
+            img = self.p_sample(
+                img, cond, torch.full((b,), i, device=device, dtype=torch.long)
+            )
+        return img
+
+    @torch.no_grad()
+    def sample(self, sample_shape=torch.Size(), cond=None):
+        if cond is not None:
+            shape = cond.shape[:-1] + (self.target_dim,)
+            # TODO reshape cond to (B*T, 1, -1)
+        else:
+            shape = sample_shape
+        x_hat = self.p_sample_loop(shape, cond)  # TODO reshape x_hat to (B,T,-1)
+
+        if self.scale is not None:
+            x_hat *= self.scale
+        return x_hat
+
+    @torch.no_grad()
+    def interpolate(self, x1, x2, t=None, lam=0.5):
+        b, *_, device = *x1.shape, x1.device
+        t = default(t, self.num_timesteps - 1)
+
+        assert x1.shape == x2.shape
+
+        t_batched = torch.stack([torch.tensor(t, device=device)] * b)
+        xt1, xt2 = map(lambda x: self.q_sample(x, t=t_batched), (x1, x2))
+
+        img = (1 - lam) * xt1 + lam * xt2
+        for i in reversed(range(0, t)):
+            img = self.p_sample(
+                img, torch.full((b,), i, device=device, dtype=torch.long)
+            )
+
+        return img
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+
+        return (
+            extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+            + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
+        )
+
+    def p_losses(self, x_start, cond, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        x_recon = self.denoise_fn(x_noisy, t, cond=cond)
+
+        if self.loss_type == "l1":
+            loss = F.l1_loss(x_recon, noise)
+        elif self.loss_type == "l2":
+            loss = F.mse_loss(x_recon, noise)
+        elif self.loss_type == "huber":
+            loss = F.smooth_l1_loss(x_recon, noise)
+        else:
+            raise NotImplementedError()
+
+        return loss
+
+    def loss(self, x, cond, *args, **kwargs):
+        if self.scale is not None:
+            x /= self.scale
+
+        B, T, _ = x.shape
+
+        time = torch.randint(0, self.num_timesteps, (B * T,), device=x.device).long()
+        loss = self.p_losses(
+            x.reshape(B * T, 1, -1), cond.reshape(B * T, 1, -1), time, *args, **kwargs
+        )
+
+        return loss
diff --git a/probts/model/nn/layers/AutoCorrelation.py b/probts/model/nn/layers/AutoCorrelation.py
new file mode 100644
index 0000000..affcd75
--- /dev/null
+++ b/probts/model/nn/layers/AutoCorrelation.py
@@ -0,0 +1,159 @@
+import torch
+import torch.nn as nn
+import math
+
+
+class AutoCorrelation(nn.Module):
+    """
+    AutoCorrelation Mechanism with the following two phases:
+    (1) period-based dependencies discovery
+    (2) time delay aggregation
+    This block can replace the self-attention family mechanism seamlessly.
+    """
+    def __init__(self, mask_flag=True, factor=1, scale=None, attention_dropout=0.1, output_attention=False):
+        super(AutoCorrelation, self).__init__()
+        self.factor = factor
+        self.scale = scale
+        self.mask_flag = mask_flag
+        self.output_attention = output_attention
+        self.dropout = nn.Dropout(attention_dropout)
+
+    def time_delay_agg_training(self, values, corr):
+        """
+        SpeedUp version of Autocorrelation (a batch-normalization style design)
+        This is for the training phase.
+        """
+        head = values.shape[1]
+        channel = values.shape[2]
+        length = values.shape[3]
+        # find top k
+        top_k = int(self.factor * math.log(length))
+        mean_value = torch.mean(torch.mean(corr, dim=1), dim=1)
+        index = torch.topk(torch.mean(mean_value, dim=0), top_k, dim=-1)[1]
+        weights = torch.stack([mean_value[:, index[i]] for i in range(top_k)], dim=-1)
+        # update corr
+        tmp_corr = torch.softmax(weights, dim=-1)
+        # aggregation
+        tmp_values = values
+        delays_agg = torch.zeros_like(values).float()
+        for i in range(top_k):
+            pattern = torch.roll(tmp_values, -int(index[i]), -1)
+            delays_agg = delays_agg + pattern * \
+                         (tmp_corr[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length))
+        return delays_agg
+
+    def time_delay_agg_inference(self, values, corr):
+        """
+        SpeedUp version of Autocorrelation (a batch-normalization style design)
+        This is for the inference phase.
+        """
+        batch = values.shape[0]
+        head = values.shape[1]
+        channel = values.shape[2]
+        length = values.shape[3]
+        # index init
+        init_index = torch.arange(length).unsqueeze(0).unsqueeze(0).unsqueeze(0)\
+            .repeat(batch, head, channel, 1).to(values.device)
+        # find top k
+        top_k = int(self.factor * math.log(length))
+        mean_value = torch.mean(torch.mean(corr, dim=1), dim=1)
+        weights, delay = torch.topk(mean_value, top_k, dim=-1)
+        # update corr
+        tmp_corr = torch.softmax(weights, dim=-1)
+        # aggregation
+        tmp_values = values.repeat(1, 1, 1, 2)
+        delays_agg = torch.zeros_like(values).float()
+        for i in range(top_k):
+            tmp_delay = init_index + delay[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length)
+            pattern = torch.gather(tmp_values, dim=-1, index=tmp_delay)
+            delays_agg = delays_agg + pattern * \
+                         (tmp_corr[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length))
+        return delays_agg
+
+    def time_delay_agg_full(self, values, corr):
+        """
+        Standard version of Autocorrelation
+        """
+        batch = values.shape[0]
+        head = values.shape[1]
+        channel = values.shape[2]
+        length = values.shape[3]
+        # index init
+        init_index = torch.arange(length).unsqueeze(0).unsqueeze(0).unsqueeze(0)\
+            .repeat(batch, head, channel, 1).to(values.device)
+        # find top k
+        top_k = int(self.factor * math.log(length))
+        weights, delay = torch.topk(corr, top_k, dim=-1)
+        # update corr
+        tmp_corr = torch.softmax(weights, dim=-1)
+        # aggregation
+        tmp_values = values.repeat(1, 1, 1, 2)
+        delays_agg = torch.zeros_like(values).float()
+        for i in range(top_k):
+            tmp_delay = init_index + delay[..., i].unsqueeze(-1)
+            pattern = torch.gather(tmp_values, dim=-1, index=tmp_delay)
+            delays_agg = delays_agg + pattern * (tmp_corr[..., i].unsqueeze(-1))
+        return delays_agg
+
+    def forward(self, queries, keys, values, attn_mask):
+        B, L, H, E = queries.shape
+        _, S, _, D = values.shape
+        if L > S:
+            zeros = torch.zeros_like(queries[:, :(L - S), :]).float()
+            values = torch.cat([values, zeros], dim=1)
+            keys = torch.cat([keys, zeros], dim=1)
+        else:
+            values = values[:, :L, :, :]
+            keys = keys[:, :L, :, :]
+
+        # period-based dependencies
+        q_fft = torch.fft.rfft(queries.permute(0, 2, 3, 1).contiguous(), dim=-1)
+        k_fft = torch.fft.rfft(keys.permute(0, 2, 3, 1).contiguous(), dim=-1)
+        res = q_fft * torch.conj(k_fft)
+        corr = torch.fft.irfft(res, n=L, dim=-1)
+
+        # time delay agg
+        if self.training:
+            V = self.time_delay_agg_training(values.permute(0, 2, 3, 1).contiguous(), corr).permute(0, 3, 1, 2)
+        else:
+            V = self.time_delay_agg_inference(values.permute(0, 2, 3, 1).contiguous(), corr).permute(0, 3, 1, 2)
+
+        if self.output_attention:
+            return (V.contiguous(), corr.permute(0, 3, 1, 2))
+        else:
+            return (V.contiguous(), None)
+
+
+class AutoCorrelationLayer(nn.Module):
+    def __init__(self, correlation, d_model, n_heads, d_keys=None,
+                 d_values=None):
+        super(AutoCorrelationLayer, self).__init__()
+
+        d_keys = d_keys or (d_model // n_heads)
+        d_values = d_values or (d_model // n_heads)
+
+        self.inner_correlation = correlation
+        self.query_projection = nn.Linear(d_model, d_keys * n_heads)
+        self.key_projection = nn.Linear(d_model, d_keys * n_heads)
+        self.value_projection = nn.Linear(d_model, d_values * n_heads)
+        self.out_projection = nn.Linear(d_values * n_heads, d_model)
+        self.n_heads = n_heads
+
+    def forward(self, queries, keys, values, attn_mask):
+        B, L, _ = queries.shape
+        _, S, _ = keys.shape
+        H = self.n_heads
+
+        queries = self.query_projection(queries).view(B, L, H, -1)
+        keys = self.key_projection(keys).view(B, S, H, -1)
+        values = self.value_projection(values).view(B, S, H, -1)
+
+        out, attn = self.inner_correlation(
+            queries,
+            keys,
+            values,
+            attn_mask
+        )
+        out = out.view(B, L, -1)
+
+        return self.out_projection(out), attn
diff --git a/probts/model/nn/layers/Autoformer_EncDec.py b/probts/model/nn/layers/Autoformer_EncDec.py
new file mode 100644
index 0000000..00b3a7a
--- /dev/null
+++ b/probts/model/nn/layers/Autoformer_EncDec.py
@@ -0,0 +1,173 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class my_Layernorm(nn.Module):
+    """
+    Special designed layernorm for the seasonal part
+    """
+    def __init__(self, channels):
+        super(my_Layernorm, self).__init__()
+        self.layernorm = nn.LayerNorm(channels)
+
+    def forward(self, x):
+        x_hat = self.layernorm(x)
+        bias = torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1)
+        return x_hat - bias
+
+
+class moving_avg(nn.Module):
+    """
+    Moving average block to highlight the trend of time series
+    """
+    def __init__(self, kernel_size, stride):
+        super(moving_avg, self).__init__()
+        self.kernel_size = kernel_size
+        self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)
+
+    def forward(self, x):
+        # padding on the both ends of time series
+        front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)
+        end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
+        x = torch.cat([front, x, end], dim=1)
+        x = self.avg(x.permute(0, 2, 1))
+        x = x.permute(0, 2, 1)
+        return x
+
+
+class series_decomp(nn.Module):
+    """
+    Series decomposition block
+    """
+    def __init__(self, kernel_size):
+        super(series_decomp, self).__init__()
+        self.moving_avg = moving_avg(kernel_size, stride=1)
+
+    def forward(self, x):
+        moving_mean = self.moving_avg(x)
+        res = x - moving_mean
+        return res, moving_mean
+
+
+class EncoderLayer(nn.Module):
+    """
+    Autoformer encoder layer with the progressive decomposition architecture
+    """
+    def __init__(self, attention, d_model, d_ff=None, moving_avg=25, dropout=0.1, activation="relu"):
+        super(EncoderLayer, self).__init__()
+        d_ff = d_ff or 4 * d_model
+        self.attention = attention
+        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1, bias=False)
+        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1, bias=False)
+        self.decomp1 = series_decomp(moving_avg)
+        self.decomp2 = series_decomp(moving_avg)
+        self.dropout = nn.Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, attn_mask=None):
+        new_x, attn = self.attention(
+            x, x, x,
+            attn_mask=attn_mask
+        )
+        x = x + self.dropout(new_x)
+        x, _ = self.decomp1(x)
+        y = x
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
+        y = self.dropout(self.conv2(y).transpose(-1, 1))
+        res, _ = self.decomp2(x + y)
+        return res, attn
+
+
+class Encoder(nn.Module):
+    """
+    Autoformer encoder
+    """
+    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
+        super(Encoder, self).__init__()
+        self.attn_layers = nn.ModuleList(attn_layers)
+        self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
+        self.norm = norm_layer
+
+    def forward(self, x, attn_mask=None):
+        attns = []
+        if self.conv_layers is not None:
+            for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers):
+                x, attn = attn_layer(x, attn_mask=attn_mask)
+                x = conv_layer(x)
+                attns.append(attn)
+            x, attn = self.attn_layers[-1](x)
+            attns.append(attn)
+        else:
+            for attn_layer in self.attn_layers:
+                x, attn = attn_layer(x, attn_mask=attn_mask)
+                attns.append(attn)
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x, attns
+
+
+class DecoderLayer(nn.Module):
+    """
+    Autoformer decoder layer with the progressive decomposition architecture
+    """
+    def __init__(self, self_attention, cross_attention, d_model, c_out, d_ff=None,
+                 moving_avg=25, dropout=0.1, activation="relu"):
+        super(DecoderLayer, self).__init__()
+        d_ff = d_ff or 4 * d_model
+        self.self_attention = self_attention
+        self.cross_attention = cross_attention
+        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1, bias=False)
+        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1, bias=False)
+        self.decomp1 = series_decomp(moving_avg)
+        self.decomp2 = series_decomp(moving_avg)
+        self.decomp3 = series_decomp(moving_avg)
+        self.dropout = nn.Dropout(dropout)
+        self.projection = nn.Conv1d(in_channels=d_model, out_channels=c_out, kernel_size=3, stride=1, padding=1,
+                                    padding_mode='circular', bias=False)
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None):
+        x = x + self.dropout(self.self_attention(
+            x, x, x,
+            attn_mask=x_mask
+        )[0])
+        x, trend1 = self.decomp1(x)
+        x = x + self.dropout(self.cross_attention(
+            x, cross, cross,
+            attn_mask=cross_mask
+        )[0])
+        x, trend2 = self.decomp2(x)
+        y = x
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
+        y = self.dropout(self.conv2(y).transpose(-1, 1))
+        x, trend3 = self.decomp3(x + y)
+
+        residual_trend = trend1 + trend2 + trend3
+        residual_trend = self.projection(residual_trend.permute(0, 2, 1)).transpose(1, 2)
+        return x, residual_trend
+
+
+class Decoder(nn.Module):
+    """
+    Autoformer encoder
+    """
+    def __init__(self, layers, norm_layer=None, projection=None):
+        super(Decoder, self).__init__()
+        self.layers = nn.ModuleList(layers)
+        self.norm = norm_layer
+        self.projection = projection
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None, trend=None):
+        for layer in self.layers:
+            x, residual_trend = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask)
+            trend = trend + residual_trend
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        if self.projection is not None:
+            x = self.projection(x)
+        return x, trend
diff --git a/probts/model/nn/layers/Conv_Blocks.py b/probts/model/nn/layers/Conv_Blocks.py
new file mode 100644
index 0000000..96ce3fc
--- /dev/null
+++ b/probts/model/nn/layers/Conv_Blocks.py
@@ -0,0 +1,60 @@
+import torch
+import torch.nn as nn
+
+
+class Inception_Block_V1(nn.Module):
+    def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True):
+        super(Inception_Block_V1, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_kernels = num_kernels
+        kernels = []
+        for i in range(self.num_kernels):
+            kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=2 * i + 1, padding=i))
+        self.kernels = nn.ModuleList(kernels)
+        if init_weight:
+            self._initialize_weights()
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        res_list = []
+        for i in range(self.num_kernels):
+            res_list.append(self.kernels[i](x))
+        res = torch.stack(res_list, dim=-1).mean(-1)
+        return res
+
+
+class Inception_Block_V2(nn.Module):
+    def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True):
+        super(Inception_Block_V2, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_kernels = num_kernels
+        kernels = []
+        for i in range(self.num_kernels // 2):
+            kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=[1, 2 * i + 3], padding=[0, i + 1]))
+            kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=[2 * i + 3, 1], padding=[i + 1, 0]))
+        kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=1))
+        self.kernels = nn.ModuleList(kernels)
+        if init_weight:
+            self._initialize_weights()
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        res_list = []
+        for i in range(self.num_kernels + 1):
+            res_list.append(self.kernels[i](x))
+        res = torch.stack(res_list, dim=-1).mean(-1)
+        return res
diff --git a/probts/model/nn/layers/Embed.py b/probts/model/nn/layers/Embed.py
new file mode 100644
index 0000000..6e7e249
--- /dev/null
+++ b/probts/model/nn/layers/Embed.py
@@ -0,0 +1,194 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils import weight_norm
+import math
+import sys
+
+class PositionalEmbedding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        super(PositionalEmbedding, self).__init__()
+        # Compute the positional encodings once in log space.
+        pe = torch.zeros(max_len, d_model).float()
+        pe.require_grad = False
+
+        position = torch.arange(0, max_len).float().unsqueeze(1)
+        div_term = (torch.arange(0, d_model, 2).float()
+                    * -(math.log(10000.0) / d_model)).exp()
+
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+
+        pe = pe.unsqueeze(0)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        return self.pe[:, :x.size(1)]
+
+
+class TokenEmbedding(nn.Module):
+    def __init__(self, c_in, d_model):
+        super(TokenEmbedding, self).__init__()
+        padding = 1 if torch.__version__ >= '1.5.0' else 2
+        self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
+                                   kernel_size=3, padding=padding, padding_mode='circular', bias=False)
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_in', nonlinearity='leaky_relu')
+
+    def forward(self, x):
+        x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
+        return x
+
+
+class FixedEmbedding(nn.Module):
+    def __init__(self, c_in, d_model):
+        super(FixedEmbedding, self).__init__()
+
+        w = torch.zeros(c_in, d_model).float()
+        w.require_grad = False
+
+        position = torch.arange(0, c_in).float().unsqueeze(1)
+        div_term = (torch.arange(0, d_model, 2).float()
+                    * -(math.log(10000.0) / d_model)).exp()
+
+        w[:, 0::2] = torch.sin(position * div_term)
+        w[:, 1::2] = torch.cos(position * div_term)
+
+        self.emb = nn.Embedding(c_in, d_model)
+        self.emb.weight = nn.Parameter(w, requires_grad=False)
+
+    def forward(self, x):
+        return self.emb(x).detach()
+
+
+class TemporalEmbedding(nn.Module):
+    def __init__(self, d_model, embed_type='fixed', freq='h'):
+        super(TemporalEmbedding, self).__init__()
+
+        minute_size = 4
+        hour_size = 24
+        weekday_size = 7
+        day_size = 32
+        month_size = 13
+
+        Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding
+        if freq == 't':
+            self.minute_embed = Embed(minute_size, d_model)
+        self.hour_embed = Embed(hour_size, d_model)
+        self.weekday_embed = Embed(weekday_size, d_model)
+        self.day_embed = Embed(day_size, d_model)
+        self.month_embed = Embed(month_size, d_model)
+
+    def forward(self, x):
+        x = x.long()
+        minute_x = self.minute_embed(x[:, :, 4]) if hasattr(
+            self, 'minute_embed') else 0.
+        hour_x = self.hour_embed(x[:, :, 3])
+        weekday_x = self.weekday_embed(x[:, :, 2])
+        day_x = self.day_embed(x[:, :, 1])
+        month_x = self.month_embed(x[:, :, 0])
+
+        return hour_x + weekday_x + day_x + month_x + minute_x
+
+
+class TimeFeatureEmbedding(nn.Module):
+    def __init__(self, d_model, embed_type='timeF', freq='h'):
+        super(TimeFeatureEmbedding, self).__init__()
+
+        if freq == 'min':
+            freq = 't'
+        freq_map = {'h': 4, 't': 5, 's': 6,
+                    'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3}
+        d_inp = freq_map[freq]
+        self.embed = nn.Linear(d_inp, d_model, bias=False)
+
+    def forward(self, x):
+        return self.embed(x)
+
+
+class DataEmbedding(nn.Module):
+    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
+        super(DataEmbedding, self).__init__()
+
+        self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
+        self.position_embedding = PositionalEmbedding(d_model=d_model)
+        self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
+                                                    freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
+            d_model=d_model, embed_type=embed_type, freq=freq)
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, x, x_mark):
+        if x_mark is None:
+            x = self.value_embedding(x) + self.position_embedding(x)
+        else:
+            x = self.value_embedding(
+                x) + self.temporal_embedding(x_mark) + self.position_embedding(x)
+        return self.dropout(x)
+
+
+class DataEmbedding_wo_pos(nn.Module):
+    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
+        super(DataEmbedding_wo_pos, self).__init__()
+
+        self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
+        self.position_embedding = PositionalEmbedding(d_model=d_model)
+        self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
+                                                    freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
+            d_model=d_model, embed_type=embed_type, freq=freq)
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, x, x_mark):
+        if x_mark is None:
+            x = self.value_embedding(x)
+        else:
+            x = self.value_embedding(x) + self.temporal_embedding(x_mark)
+        return self.dropout(x)
+
+
+class PatchEmbedding(nn.Module):
+    def __init__(self, d_model, patch_len, stride, padding, dropout):
+        super(PatchEmbedding, self).__init__()
+        # Patching
+        self.patch_len = patch_len
+        self.stride = stride
+        self.padding_patch_layer = nn.ReplicationPad1d((0, padding))
+
+        # Backbone, Input encoding: projection of feature vectors onto a d-dim vector space
+        self.value_embedding = nn.Linear(patch_len, d_model, bias=False)
+
+        # Positional embedding
+        self.position_embedding = PositionalEmbedding(d_model)
+
+        # Residual dropout
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        # do patching
+        n_vars = x.shape[1]
+        x = self.padding_patch_layer(x)
+        x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
+        x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
+        # Input encoding
+        x = self.value_embedding(x) + self.position_embedding(x)
+        return self.dropout(x), n_vars
+
+
+# Code implementation from https://github.com/thuml/iTransformer
+class DataEmbedding_inverted(nn.Module):
+    def __init__(self, c_in, d_model, dropout=0.1):
+        super(DataEmbedding_inverted, self).__init__()
+        self.value_embedding = nn.Linear(c_in, d_model)
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, x, x_mark):
+        x = x.permute(0, 2, 1)
+        # x: [Batch Variate Time]
+        if x_mark is None:
+            x = self.value_embedding(x)
+        else:
+            # the potential to take covariates (e.g. timestamps) as tokens
+            x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1)) 
+        # x: [Batch Variate d_model]
+        return self.dropout(x)
diff --git a/probts/model/nn/layers/Moirai_backbone.py b/probts/model/nn/layers/Moirai_backbone.py
new file mode 100644
index 0000000..588a124
--- /dev/null
+++ b/probts/model/nn/layers/Moirai_backbone.py
@@ -0,0 +1,513 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from uni2ts
+# - Source: https://github.com/SalesforceAIResearch/uni2ts
+# - Paper: Unified Training of Universal Time Series Forecasting Transformers
+# - License: Apache License 2.0
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import math
+from contextlib import contextmanager
+from copy import deepcopy
+from typing import Any, Generator, Optional
+
+import lightning as L
+import torch
+from einops import rearrange, reduce, repeat
+from jaxtyping import Bool, Float, Int
+from torch.distributions import Distribution
+
+from uni2ts.common.torch_util import safe_div
+from uni2ts.loss.packed import PackedNLLLoss as _PackedNLLLoss
+from uni2ts.model.moirai.module import MoiraiModule
+from uni2ts.module.packed_scaler import PackedNOPScaler, PackedStdScaler
+
+
+class SampleNLLLoss(_PackedNLLLoss):
+    def reduce_loss(
+        self,
+        loss: Float[torch.Tensor, "batch seq_len #dim"],
+        prediction_mask: Optional[Bool[torch.Tensor, "batch seq_len"]],
+        observed_mask: Optional[Bool[torch.Tensor, "batch seq_len #dim"]],
+        sample_id: Optional[Int[torch.Tensor, "batch seq_len"]],
+        variate_id: Optional[Int[torch.Tensor, "batch seq_len"]],
+    ) -> Float[torch.Tensor, "batch"]:
+        id_mask = torch.logical_and(
+            torch.eq(sample_id.unsqueeze(-1), sample_id.unsqueeze(-2)),
+            torch.eq(variate_id.unsqueeze(-1), variate_id.unsqueeze(-2)),
+        )
+        mask = prediction_mask.unsqueeze(-1) * observed_mask
+        tobs = reduce(
+            id_mask
+            * reduce(
+                mask,
+                "... seq dim -> ... 1 seq",
+                "sum",
+            ),
+            "... seq1 seq2 -> ... seq1 1",
+            "sum",
+        )
+        loss = safe_div(loss, tobs)
+        return (loss * mask).sum(dim=(-1, -2))
+
+
+class MoiraiBackbone(L.LightningModule):
+    def __init__(
+        self,
+        prediction_length: int,
+        target_dim: int,
+        context_length: int,
+        module_kwargs: Optional[dict[str, Any]] = None,
+        module: Optional[MoiraiModule] = None,
+        patch_size: int | str = "auto",
+        num_samples: int = 100,
+        scaling: bool = True,
+    ):
+        assert (module is not None) or (
+            module_kwargs is not None
+        ), "if module is not provided, module_kwargs is required"
+        super().__init__()
+        self.save_hyperparameters(ignore=["module"])
+        self.module = MoiraiModule(**module_kwargs) if module is None else module
+        self.module.scaling = scaling
+        self.module.scaler = PackedStdScaler() if scaling else PackedNOPScaler()
+        self.per_sample_loss_func = SampleNLLLoss()
+
+    @contextmanager
+    def hparams_context(
+        self,
+        prediction_length: Optional[int] = None,
+        target_dim: Optional[int] = None,
+        context_length: Optional[int] = None,
+        patch_size: Optional[int | str] = None,
+        num_samples: Optional[int] = None,
+    ) -> Generator["MoiraiForecast", None, None]:
+        kwargs = {
+            "prediction_length": prediction_length,
+            "target_dim": target_dim,
+            "context_length": context_length,
+            "patch_size": patch_size,
+            "num_samples": num_samples,
+        }
+        old_hparams = deepcopy(self.hparams)
+        for kw, arg in kwargs.items():
+            if arg is not None:
+                self.hparams[kw] = arg
+
+        yield self
+
+        for kw in kwargs:
+            self.hparams[kw] = old_hparams[kw]
+
+    @property
+    def past_length(self) -> int:
+        return (
+            self.hparams.context_length + self.hparams.prediction_length
+            if self.hparams.patch_size == "auto"
+            else self.hparams.context_length
+        )
+
+    def context_token_length(self, patch_size: int) -> int:
+        return math.ceil(self.hparams.context_length / patch_size)
+
+    def prediction_token_length(self, patch_size) -> int:
+        return math.ceil(self.hparams.prediction_length / patch_size)
+
+    @property
+    def max_patch_size(self) -> int:
+        return max(self.module.patch_sizes)
+
+    def forward(
+        self,
+        past_target: Float[torch.Tensor, "batch past_time tgt"],
+        past_observed_target: Bool[torch.Tensor, "batch past_time tgt"],
+        past_is_pad: Bool[torch.Tensor, "batch past_time"],
+        num_samples: Optional[int] = None,
+    ) -> Float[torch.Tensor, "batch sample future_time *tgt"]:
+        if self.hparams.patch_size == "auto":
+            val_loss = []
+            preds = []
+            for patch_size in self.module.patch_sizes:
+                val_loss.append(
+                    self._val_loss(
+                        patch_size=patch_size,
+                        target=past_target[..., : self.past_length, :],
+                        observed_target=past_observed_target[
+                            ..., : self.past_length, :
+                        ],
+                        is_pad=past_is_pad[..., : self.past_length]
+                    )
+                )
+                distr = self._get_distr(
+                    patch_size,
+                    past_target[..., -self.hparams.context_length :, :],
+                    past_observed_target[..., -self.hparams.context_length :, :],
+                    past_is_pad[..., -self.hparams.context_length :]
+                )
+                preds.append(
+                    self._format_preds(
+                        patch_size,
+                        distr.sample(
+                            torch.Size((num_samples or self.hparams.num_samples,))
+                        ),
+                        past_target.shape[-1],
+                    )
+                )
+            val_loss = torch.stack(val_loss)
+            preds = torch.stack(preds)
+            idx = val_loss.argmin(dim=0)
+            return preds[idx, torch.arange(len(idx), device=idx.device)]
+        else:
+            distr = self._get_distr(
+                self.hparams.patch_size,
+                past_target[..., -self.hparams.context_length :, :],
+                past_observed_target[..., -self.hparams.context_length :, :],
+                past_is_pad[..., -self.hparams.context_length :],
+            )
+            preds = distr.sample(torch.Size((num_samples or self.hparams.num_samples,)))
+            return self._format_preds(
+                self.hparams.patch_size, preds, past_target.shape[-1]
+            )
+
+    def _val_loss(
+        self,
+        patch_size: int,
+        target: Float[torch.Tensor, "batch time tgt"],
+        observed_target: Bool[torch.Tensor, "batch time tgt"],
+        is_pad: Bool[torch.Tensor, "batch time"]
+    ) -> Float[torch.Tensor, "batch"]:
+        # convert format
+        (
+            target,
+            observed_mask,
+            sample_id,
+            time_id,
+            variate_id,
+            prediction_mask,
+        ) = self._convert(
+            patch_size,
+            past_target=target[..., : self.hparams.context_length, :],
+            past_observed_target=observed_target[..., : self.hparams.context_length, :],
+            past_is_pad=is_pad[..., : self.hparams.context_length],
+            future_target=target[..., self.hparams.context_length :, :],
+            future_observed_target=observed_target[
+                ..., self.hparams.context_length :, :
+            ],
+            future_is_pad=is_pad[..., self.hparams.context_length :]
+        )
+        # get predictions
+        distr = self.module(
+            target,
+            observed_mask,
+            sample_id,
+            time_id,
+            variate_id,
+            prediction_mask,
+            torch.ones_like(time_id, dtype=torch.long) * patch_size,
+        )
+        val_loss = self.per_sample_loss_func(
+            pred=distr,
+            target=target,
+            prediction_mask=prediction_mask,
+            observed_mask=observed_mask,
+            sample_id=sample_id,
+            variate_id=variate_id,
+        )
+        return val_loss
+
+    def _get_distr(
+        self,
+        patch_size: int,
+        past_target: Float[torch.Tensor, "batch past_time tgt"],
+        past_observed_target: Bool[torch.Tensor, "batch past_time tgt"],
+        past_is_pad: Bool[torch.Tensor, "batch past_time"]
+    ) -> Distribution:
+        # convert format
+        (
+            target,
+            observed_mask,
+            sample_id,
+            time_id,
+            variate_id,
+            prediction_mask,
+        ) = self._convert(
+            patch_size,
+            past_target,
+            past_observed_target,
+            past_is_pad
+        )
+        # get predictions
+        distr = self.module(
+            target,
+            observed_mask,
+            sample_id,
+            time_id,
+            variate_id,
+            prediction_mask,
+            torch.ones_like(time_id, dtype=torch.long) * patch_size,
+        )
+        return distr
+
+    @staticmethod
+    def _patched_seq_pad(
+        patch_size: int,
+        x: torch.Tensor,
+        dim: int,
+        left: bool = True,
+        value: Optional[float] = None,
+    ) -> torch.Tensor:
+        if dim >= 0:
+            dim = -x.ndim + dim
+        pad_length = -x.size(dim) % patch_size
+        if left:
+            pad = (pad_length, 0)
+        else:
+            pad = (0, pad_length)
+        pad = (0, 0) * (abs(dim) - 1) + pad
+        return torch.nn.functional.pad(x, pad, value=value)
+
+    def _generate_time_id(
+        self,
+        patch_size: int,
+        past_observed_target: Bool[torch.Tensor, "batch past_seq tgt"],
+        future_target: Float[torch.Tensor, "batch future_seq tgt"],
+    ) -> tuple[
+        Int[torch.Tensor, "batch past_token"], Int[torch.Tensor, "batch future_token"]
+    ]:
+        past_seq_id = reduce(
+            self._patched_seq_pad(patch_size, past_observed_target, -2, left=True),
+            "... (seq patch) dim -> ... seq",
+            "max",
+            patch=patch_size,
+        )
+        past_seq_id = torch.clamp(past_seq_id.cumsum(dim=-1) - 1, min=0)
+        batch_shape = " ".join(map(str, past_observed_target.shape[:-2]))
+        future_seq_id = (
+            repeat(
+                torch.arange(
+                    math.ceil(future_target.shape[-2] / patch_size),
+                    device=past_observed_target.device,
+                ),
+                f"prediction -> {batch_shape} prediction",
+            )
+            + past_seq_id.max(dim=-1, keepdim=True).values
+            + 1
+        )
+        past_seq_id = past_seq_id.to(dtype=torch.int32)
+        future_seq_id = future_seq_id.to(dtype=torch.int32)
+        return past_seq_id, future_seq_id
+
+    def _convert(
+        self,
+        patch_size: int,
+        past_target: Float[torch.Tensor, "batch past_time tgt"],
+        past_observed_target: Bool[torch.Tensor, "batch past_time tgt"],
+        past_is_pad: Bool[torch.Tensor, "batch past_time"],
+        future_target: Optional[Float[torch.Tensor, "batch future_time tgt"]] = None,
+        future_observed_target: Optional[
+            Bool[torch.Tensor, "batch future_time tgt"]
+        ] = None,
+        future_is_pad: Optional[Bool[torch.Tensor, "batch future_time"]] = None
+    ) -> tuple[
+        Float[torch.Tensor, "batch combine_seq patch"],  # target
+        Bool[torch.Tensor, "batch combine_seq patch"],  # observed_mask
+        Int[torch.Tensor, "batch combine_seq"],  # sample_id
+        Int[torch.Tensor, "batch combine_seq"],  # time_id
+        Int[torch.Tensor, "batch combine_seq"],  # variate_id
+        Bool[torch.Tensor, "batch combine_seq"],  # prediction_mask
+    ]:
+        batch_shape = past_target.shape[:-2]
+        device = past_target.device
+
+        target = []
+        observed_mask = []
+        sample_id = []
+        time_id = []
+        variate_id = []
+        prediction_mask = []
+        dim_count = 0
+
+        if future_target is None:
+            future_target = torch.zeros(
+                batch_shape
+                + (
+                    self.hparams.prediction_length,
+                    past_target.shape[-1],
+                ),
+                dtype=past_target.dtype,
+                device=device,
+            )
+        
+        past_seq_id, future_seq_id = self._generate_time_id(
+            patch_size, past_observed_target, future_target
+        )
+
+        target.extend(
+            [
+                torch.nn.functional.pad(
+                    rearrange(
+                        self._patched_seq_pad(patch_size, past_target, -2, left=True),
+                        "... (seq patch) dim -> ... (dim seq) patch",
+                        patch=patch_size,
+                    ),
+                    (0, self.max_patch_size - patch_size),
+                ),
+                torch.nn.functional.pad(
+                    rearrange(
+                        self._patched_seq_pad(
+                            patch_size, future_target, -2, left=False
+                        ),
+                        "... (seq patch) dim -> ... (dim seq) patch",
+                        patch=patch_size,
+                    ),
+                    (0, self.max_patch_size - patch_size),
+                ),
+            ]
+        )
+        if future_observed_target is None:
+            future_observed_target = torch.ones(
+                batch_shape
+                + (
+                    self.hparams.prediction_length,
+                    past_observed_target.shape[-1],
+                ),
+                dtype=torch.bool,
+                device=device,
+            )
+        observed_mask.extend(
+            [
+                torch.nn.functional.pad(
+                    rearrange(
+                        self._patched_seq_pad(
+                            patch_size, past_observed_target, -2, left=True
+                        ),
+                        "... (seq patch) dim -> ... (dim seq) patch",
+                        patch=patch_size,
+                    ),
+                    (0, self.max_patch_size - patch_size),
+                ),
+                torch.nn.functional.pad(
+                    rearrange(
+                        self._patched_seq_pad(
+                            patch_size, future_observed_target, -2, left=False
+                        ),
+                        "... (seq patch) dim -> ... (dim seq) patch",
+                        patch=patch_size,
+                    ),
+                    (0, self.max_patch_size - patch_size),
+                ),
+            ]
+        )
+        if future_is_pad is None:
+            future_is_pad = torch.zeros(
+                batch_shape + (self.hparams.prediction_length,),
+                dtype=torch.long,
+                device=device,
+            )
+        sample_id.extend(
+            [
+                repeat(
+                    reduce(
+                        (
+                            self._patched_seq_pad(
+                                patch_size, past_is_pad, -1, left=True, value=1
+                            )
+                            == 0
+                        ).int(),
+                        "... (seq patch) -> ... seq",
+                        "max",
+                        patch=patch_size,
+                    ),
+                    "... seq -> ... (dim seq)",
+                    dim=past_target.shape[-1],
+                ),
+                repeat(
+                    reduce(
+                        (
+                            self._patched_seq_pad(
+                                patch_size, future_is_pad, -1, left=False, value=1
+                            )
+                            == 0
+                        ).int(),
+                        "... (seq patch) -> ... seq",
+                        "max",
+                        patch=patch_size,
+                    ),
+                    "... seq -> ... (dim seq)",
+                    dim=past_target.shape[-1],
+                ),
+            ]
+        )
+        time_id.extend(
+            [past_seq_id] * past_target.shape[-1]
+            + [future_seq_id] * past_target.shape[-1]
+        )
+        variate_id.extend(
+            [
+                repeat(
+                    torch.arange(past_target.shape[-1], device=device) + dim_count,
+                    f"dim -> {' '.join(map(str, batch_shape))} (dim past)",
+                    past=self.context_token_length(patch_size),
+                ),
+                repeat(
+                    torch.arange(past_target.shape[-1], device=device) + dim_count,
+                    f"dim -> {' '.join(map(str, batch_shape))} (dim future)",
+                    # future=self.prediction_token_length(patch_size),
+                    future = math.ceil(future_target.shape[-2] / patch_size)
+                ),
+            ]
+        )
+        dim_count += past_target.shape[-1]
+        prediction_mask.extend(
+            [
+                torch.zeros(
+                    batch_shape
+                    + (self.context_token_length(patch_size) * past_target.shape[-1],),
+                    dtype=torch.bool,
+                    device=device,
+                ),
+                torch.ones(
+                    batch_shape
+                    + (
+                        # self.prediction_token_length(patch_size)
+                        math.ceil(future_target.shape[-2] / patch_size)
+                        * past_target.shape[-1],
+                    ),
+                    dtype=torch.bool,
+                    device=device,
+                ),
+            ]
+        )
+
+        target = torch.cat(target, dim=-2)
+        observed_mask = torch.cat(observed_mask, dim=-2)
+        sample_id = torch.cat(sample_id, dim=-1)
+        time_id = torch.cat(time_id, dim=-1)
+        variate_id = torch.cat(variate_id, dim=-1)
+        prediction_mask = torch.cat(prediction_mask, dim=-1)
+        return (
+            target,
+            observed_mask,
+            sample_id,
+            time_id,
+            variate_id,
+            prediction_mask,
+        )
+
+    def _format_preds(
+        self,
+        patch_size: int,
+        preds: Float[torch.Tensor, "sample batch combine_seq patch"],
+        target_dim: int,
+    ) -> Float[torch.Tensor, "batch sample future_time *tgt"]:
+        start = target_dim * self.context_token_length(patch_size)
+        end = start + target_dim * self.prediction_token_length(patch_size)
+        preds = preds[..., start:end, :patch_size]
+        preds = rearrange(
+            preds,
+            "sample ... (dim seq) patch -> ... sample (seq patch) dim",
+            dim=target_dim,
+        )[..., : self.hparams.prediction_length, :]
+        return preds.squeeze(-1)
\ No newline at end of file
diff --git a/probts/model/nn/layers/PatchTST_backbone.py b/probts/model/nn/layers/PatchTST_backbone.py
new file mode 100644
index 0000000..3394a55
--- /dev/null
+++ b/probts/model/nn/layers/PatchTST_backbone.py
@@ -0,0 +1,387 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PatchTST
+# - Source: https://github.com/yuqinie98/PatchTST/tree/main
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+__all__ = ['PatchTST_backbone']
+
+# Cell
+from typing import Callable, Optional
+import torch
+from torch import nn
+from torch import Tensor
+import torch.nn.functional as F
+import numpy as np
+
+#from collections import OrderedDict
+from .PatchTST_layers import *
+from .RevIN import RevIN
+
+# Cell
+class PatchTST_backbone(nn.Module):
+    def __init__(self, c_in:int, context_window:int, target_window:int, patch_len:int, stride:int, max_seq_len:Optional[int]=1024, 
+                 n_layers:int=3, d_model=128, n_heads=16, d_k:Optional[int]=None, d_v:Optional[int]=None,
+                 d_ff:int=256, norm:str='BatchNorm', attn_dropout:float=0., dropout:float=0., act:str="gelu", key_padding_mask:bool='auto',
+                 padding_var:Optional[int]=None, attn_mask:Optional[Tensor]=None, res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False,
+                 pe:str='zeros', learn_pe:bool=True, fc_dropout:float=0., head_dropout = 0, padding_patch = None,
+                 pretrain_head:bool=False, head_type = 'flatten', individual = False, revin = True, affine = True, subtract_last = False,
+                 verbose:bool=False):
+        
+        super().__init__()
+        
+        # RevIn
+        self.revin = revin
+        if self.revin: self.revin_layer = RevIN(c_in, affine=affine, subtract_last=subtract_last)
+        
+        # Patching
+        self.patch_len = patch_len
+        self.stride = stride
+        self.padding_patch = padding_patch
+        patch_num = int((context_window - patch_len)/stride + 1)
+        if padding_patch == 'end': # can be modified to general case
+            self.padding_patch_layer = nn.ReplicationPad1d((0, stride)) 
+            patch_num += 1
+        
+        # Backbone 
+        self.backbone = TSTiEncoder(c_in, patch_num=patch_num, patch_len=patch_len, max_seq_len=max_seq_len,
+                                n_layers=n_layers, d_model=d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff,
+                                attn_dropout=attn_dropout, dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var,
+                                attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
+                                pe=pe, learn_pe=learn_pe, verbose=verbose)
+
+        # Head
+        self.head_nf = d_model * patch_num
+        self.n_vars = c_in
+        self.pretrain_head = pretrain_head
+        self.head_type = head_type
+        self.individual = individual
+
+        if self.pretrain_head: 
+            self.head = self.create_pretrain_head(self.head_nf, c_in, fc_dropout) # custom head passed as a partial func with all its kwargs
+        elif head_type == 'flatten': 
+            self.head = Flatten_Head(self.individual, self.n_vars, self.head_nf, target_window, head_dropout=head_dropout)
+        
+    
+    def forward(self, z):                                                                   # z: [bs x nvars x seq_len]
+        # norm
+        if self.revin: 
+            z = z.permute(0,2,1)
+            z = self.revin_layer(z, 'norm')
+            z = z.permute(0,2,1)
+            
+        # do patching
+        if self.padding_patch == 'end':
+            z = self.padding_patch_layer(z)
+        z = z.unfold(dimension=-1, size=self.patch_len, step=self.stride)                   # z: [bs x nvars x patch_num x patch_len]
+        z = z.permute(0,1,3,2)                                                              # z: [bs x nvars x patch_len x patch_num]
+        
+        # model
+        z = self.backbone(z)                                                                # z: [bs x nvars x d_model x patch_num]
+        z = self.head(z)                                                                    # z: [bs x nvars x target_window] 
+        
+        # denorm
+        if self.revin: 
+            z = z.permute(0,2,1)
+            z = self.revin_layer(z, 'denorm')
+            z = z.permute(0,2,1)
+        return z
+    
+    def create_pretrain_head(self, head_nf, vars, dropout):
+        return nn.Sequential(nn.Dropout(dropout),
+                    nn.Conv1d(head_nf, vars, 1)
+                    )
+
+
+class Flatten_Head(nn.Module):
+    def __init__(self, individual, n_vars, nf, target_window, head_dropout=0):
+        super().__init__()
+        
+        self.individual = individual
+        self.n_vars = n_vars
+        
+        if self.individual:
+            self.linears = nn.ModuleList()
+            self.dropouts = nn.ModuleList()
+            self.flattens = nn.ModuleList()
+            for i in range(self.n_vars):
+                self.flattens.append(nn.Flatten(start_dim=-2))
+                self.linears.append(nn.Linear(nf, target_window))
+                self.dropouts.append(nn.Dropout(head_dropout))
+        else:
+            self.flatten = nn.Flatten(start_dim=-2)
+            self.linear = nn.Linear(nf, target_window)
+            self.dropout = nn.Dropout(head_dropout)
+            
+    def forward(self, x):                                 # x: [bs x nvars x d_model x patch_num]
+        if self.individual:
+            x_out = []
+            for i in range(self.n_vars):
+                z = self.flattens[i](x[:,i,:,:])          # z: [bs x d_model * patch_num]
+                z = self.linears[i](z)                    # z: [bs x target_window]
+                z = self.dropouts[i](z)
+                x_out.append(z)
+            x = torch.stack(x_out, dim=1)                 # x: [bs x nvars x target_window]
+        else:
+            x = self.flatten(x)
+            x = self.linear(x)
+            x = self.dropout(x)
+        return x
+        
+        
+    
+    
+class TSTiEncoder(nn.Module):  #i means channel-independent
+    def __init__(self, c_in, patch_num, patch_len, max_seq_len=1024,
+                 n_layers=3, d_model=128, n_heads=16, d_k=None, d_v=None,
+                 d_ff=256, norm='BatchNorm', attn_dropout=0., dropout=0., act="gelu", store_attn=False,
+                 key_padding_mask='auto', padding_var=None, attn_mask=None, res_attention=True, pre_norm=False,
+                 pe='zeros', learn_pe=True, verbose=False):
+        
+        
+        super().__init__()
+        
+        self.patch_num = patch_num
+        self.patch_len = patch_len
+        
+        # Input encoding
+        q_len = patch_num
+        self.W_P = nn.Linear(patch_len, d_model)        # Eq 1: projection of feature vectors onto a d-dim vector space
+        self.seq_len = q_len
+
+        # Positional encoding
+        self.W_pos = positional_encoding(pe, learn_pe, q_len, d_model)
+
+        # Residual dropout
+        self.dropout = nn.Dropout(dropout)
+
+        # Encoder
+        self.encoder = TSTEncoder(q_len, d_model, n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, dropout=dropout,
+                                   pre_norm=pre_norm, activation=act, res_attention=res_attention, n_layers=n_layers, store_attn=store_attn)
+
+        
+    def forward(self, x) -> Tensor:                                              # x: [bs x nvars x patch_len x patch_num]
+        
+        n_vars = x.shape[1]
+        # Input encoding
+        x = x.permute(0,1,3,2)                                                   # x: [bs x nvars x patch_num x patch_len]
+        x = self.W_P(x)                                                          # x: [bs x nvars x patch_num x d_model]
+
+        u = torch.reshape(x, (x.shape[0]*x.shape[1],x.shape[2],x.shape[3]))      # u: [bs * nvars x patch_num x d_model]
+        u = self.dropout(u + self.W_pos)                                         # u: [bs * nvars x patch_num x d_model]
+
+        # Encoder
+        z = self.encoder(u)                                                      # z: [bs * nvars x patch_num x d_model]
+        z = torch.reshape(z, (-1,n_vars,z.shape[-2],z.shape[-1]))                # z: [bs x nvars x patch_num x d_model]
+        z = z.permute(0,1,3,2)                                                   # z: [bs x nvars x d_model x patch_num]
+        
+        return z    
+            
+            
+    
+# Cell
+class TSTEncoder(nn.Module):
+    def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=None, 
+                        norm='BatchNorm', attn_dropout=0., dropout=0., activation='gelu',
+                        res_attention=False, n_layers=1, pre_norm=False, store_attn=False):
+        super().__init__()
+
+        self.layers = nn.ModuleList([TSTEncoderLayer(q_len, d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm,
+                                                      attn_dropout=attn_dropout, dropout=dropout,
+                                                      activation=activation, res_attention=res_attention,
+                                                      pre_norm=pre_norm, store_attn=store_attn) for i in range(n_layers)])
+        self.res_attention = res_attention
+
+    def forward(self, src:Tensor, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None):
+        output = src
+        scores = None
+        if self.res_attention:
+            for mod in self.layers: output, scores = mod(output, prev=scores, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
+            return output
+        else:
+            for mod in self.layers: output = mod(output, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
+            return output
+
+
+
+class TSTEncoderLayer(nn.Module):
+    def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=256, store_attn=False,
+                 norm='BatchNorm', attn_dropout=0, dropout=0., bias=True, activation="gelu", res_attention=False, pre_norm=False):
+        super().__init__()
+        assert not d_model%n_heads, f"d_model ({d_model}) must be divisible by n_heads ({n_heads})"
+        d_k = d_model // n_heads if d_k is None else d_k
+        d_v = d_model // n_heads if d_v is None else d_v
+
+        # Multi-Head attention
+        self.res_attention = res_attention
+        self.self_attn = _MultiheadAttention(d_model, n_heads, d_k, d_v, attn_dropout=attn_dropout, proj_dropout=dropout, res_attention=res_attention)
+
+        # Add & Norm
+        self.dropout_attn = nn.Dropout(dropout)
+        if "batch" in norm.lower():
+            self.norm_attn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
+        else:
+            self.norm_attn = nn.LayerNorm(d_model)
+
+        # Position-wise Feed-Forward
+        self.ff = nn.Sequential(nn.Linear(d_model, d_ff, bias=bias),
+                                get_activation_fn(activation),
+                                nn.Dropout(dropout),
+                                nn.Linear(d_ff, d_model, bias=bias))
+
+        # Add & Norm
+        self.dropout_ffn = nn.Dropout(dropout)
+        if "batch" in norm.lower():
+            self.norm_ffn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
+        else:
+            self.norm_ffn = nn.LayerNorm(d_model)
+
+        self.pre_norm = pre_norm
+        self.store_attn = store_attn
+
+
+    def forward(self, src:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None) -> Tensor:
+
+        # Multi-Head attention sublayer
+        if self.pre_norm:
+            src = self.norm_attn(src)
+        ## Multi-Head attention
+        if self.res_attention:
+            src2, attn, scores = self.self_attn(src, src, src, prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
+        else:
+            src2, attn = self.self_attn(src, src, src, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
+        if self.store_attn:
+            self.attn = attn
+        ## Add & Norm
+        src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout
+        if not self.pre_norm:
+            src = self.norm_attn(src)
+
+        # Feed-forward sublayer
+        if self.pre_norm:
+            src = self.norm_ffn(src)
+        ## Position-wise Feed-Forward
+        src2 = self.ff(src)
+        ## Add & Norm
+        src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout
+        if not self.pre_norm:
+            src = self.norm_ffn(src)
+
+        if self.res_attention:
+            return src, scores
+        else:
+            return src
+
+
+
+
+class _MultiheadAttention(nn.Module):
+    def __init__(self, d_model, n_heads, d_k=None, d_v=None, res_attention=False, attn_dropout=0., proj_dropout=0., qkv_bias=True, lsa=False):
+        """Multi Head Attention Layer
+        Input shape:
+            Q:       [batch_size (bs) x max_q_len x d_model]
+            K, V:    [batch_size (bs) x q_len x d_model]
+            mask:    [q_len x q_len]
+        """
+        super().__init__()
+        d_k = d_model // n_heads if d_k is None else d_k
+        d_v = d_model // n_heads if d_v is None else d_v
+
+        self.n_heads, self.d_k, self.d_v = n_heads, d_k, d_v
+
+        self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias)
+        self.W_K = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias)
+        self.W_V = nn.Linear(d_model, d_v * n_heads, bias=qkv_bias)
+
+        # Scaled Dot-Product Attention (multiple heads)
+        self.res_attention = res_attention
+        self.sdp_attn = _ScaledDotProductAttention(d_model, n_heads, attn_dropout=attn_dropout, res_attention=self.res_attention, lsa=lsa)
+
+        # Poject output
+        self.to_out = nn.Sequential(nn.Linear(n_heads * d_v, d_model), nn.Dropout(proj_dropout))
+
+
+    def forward(self, Q:Tensor, K:Optional[Tensor]=None, V:Optional[Tensor]=None, prev:Optional[Tensor]=None,
+                key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None):
+
+        bs = Q.size(0)
+        if K is None: K = Q
+        if V is None: V = Q
+
+        # Linear (+ split in multiple heads)
+        q_s = self.W_Q(Q).view(bs, -1, self.n_heads, self.d_k).transpose(1,2)       # q_s    : [bs x n_heads x max_q_len x d_k]
+        k_s = self.W_K(K).view(bs, -1, self.n_heads, self.d_k).permute(0,2,3,1)     # k_s    : [bs x n_heads x d_k x q_len] - transpose(1,2) + transpose(2,3)
+        v_s = self.W_V(V).view(bs, -1, self.n_heads, self.d_v).transpose(1,2)       # v_s    : [bs x n_heads x q_len x d_v]
+
+        # Apply Scaled Dot-Product Attention (multiple heads)
+        if self.res_attention:
+            output, attn_weights, attn_scores = self.sdp_attn(q_s, k_s, v_s, prev=prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
+        else:
+            output, attn_weights = self.sdp_attn(q_s, k_s, v_s, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
+        # output: [bs x n_heads x q_len x d_v], attn: [bs x n_heads x q_len x q_len], scores: [bs x n_heads x max_q_len x q_len]
+
+        # back to the original inputs dimensions
+        output = output.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.d_v) # output: [bs x q_len x n_heads * d_v]
+        output = self.to_out(output)
+
+        if self.res_attention: return output, attn_weights, attn_scores
+        else: return output, attn_weights
+
+
+class _ScaledDotProductAttention(nn.Module):
+    r"""Scaled Dot-Product Attention module (Attention is all you need by Vaswani et al., 2017) with optional residual attention from previous layer
+    (Realformer: Transformer likes residual attention by He et al, 2020) and locality self sttention (Vision Transformer for Small-Size Datasets
+    by Lee et al, 2021)"""
+
+    def __init__(self, d_model, n_heads, attn_dropout=0., res_attention=False, lsa=False):
+        super().__init__()
+        self.attn_dropout = nn.Dropout(attn_dropout)
+        self.res_attention = res_attention
+        head_dim = d_model // n_heads
+        self.scale = nn.Parameter(torch.tensor(head_dim ** -0.5), requires_grad=lsa)
+        self.lsa = lsa
+
+    def forward(self, q:Tensor, k:Tensor, v:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None):
+        '''
+        Input shape:
+            q               : [bs x n_heads x max_q_len x d_k]
+            k               : [bs x n_heads x d_k x seq_len]
+            v               : [bs x n_heads x seq_len x d_v]
+            prev            : [bs x n_heads x q_len x seq_len]
+            key_padding_mask: [bs x seq_len]
+            attn_mask       : [1 x seq_len x seq_len]
+        Output shape:
+            output:  [bs x n_heads x q_len x d_v]
+            attn   : [bs x n_heads x q_len x seq_len]
+            scores : [bs x n_heads x q_len x seq_len]
+        '''
+
+        # Scaled MatMul (q, k) - similarity scores for all pairs of positions in an input sequence
+        attn_scores = torch.matmul(q, k) * self.scale      # attn_scores : [bs x n_heads x max_q_len x q_len]
+
+        # Add pre-softmax attention scores from the previous layer (optional)
+        if prev is not None: attn_scores = attn_scores + prev
+
+        # Attention mask (optional)
+        if attn_mask is not None:                                     # attn_mask with shape [q_len x seq_len] - only used when q_len == seq_len
+            if attn_mask.dtype == torch.bool:
+                attn_scores.masked_fill_(attn_mask, -np.inf)
+            else:
+                attn_scores += attn_mask
+
+        # Key padding mask (optional)
+        if key_padding_mask is not None:                              # mask with shape [bs x q_len] (only when max_w_len == q_len)
+            attn_scores.masked_fill_(key_padding_mask.unsqueeze(1).unsqueeze(2), -np.inf)
+
+        # normalize the attention weights
+        attn_weights = F.softmax(attn_scores, dim=-1)                 # attn_weights   : [bs x n_heads x max_q_len x q_len]
+        attn_weights = self.attn_dropout(attn_weights)
+
+        # compute the new values given the attention weights
+        output = torch.matmul(attn_weights, v)                        # output: [bs x n_heads x max_q_len x d_v]
+
+        if self.res_attention: return output, attn_weights, attn_scores
+        else: return output, attn_weights
+
diff --git a/probts/model/nn/layers/PatchTST_layers.py b/probts/model/nn/layers/PatchTST_layers.py
new file mode 100644
index 0000000..4fffcb6
--- /dev/null
+++ b/probts/model/nn/layers/PatchTST_layers.py
@@ -0,0 +1,129 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PatchTST
+# - Source: https://github.com/yuqinie98/PatchTST/tree/main
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+__all__ = ['Transpose', 'get_activation_fn', 'moving_avg', 'series_decomp', 'PositionalEncoding', 'SinCosPosEncoding', 'Coord2dPosEncoding', 'Coord1dPosEncoding', 'positional_encoding']           
+
+import torch
+from torch import nn
+import math
+
+class Transpose(nn.Module):
+    def __init__(self, *dims, contiguous=False): 
+        super().__init__()
+        self.dims, self.contiguous = dims, contiguous
+    def forward(self, x):
+        if self.contiguous: return x.transpose(*self.dims).contiguous()
+        else: return x.transpose(*self.dims)
+
+    
+def get_activation_fn(activation):
+    if callable(activation): return activation()
+    elif activation.lower() == "relu": return nn.ReLU()
+    elif activation.lower() == "gelu": return nn.GELU()
+    raise ValueError(f'{activation} is not available. You can use "relu", "gelu", or a callable') 
+    
+    
+# decomposition
+
+class moving_avg(nn.Module):
+    """
+    Moving average block to highlight the trend of time series
+    """
+    def __init__(self, kernel_size, stride):
+        super(moving_avg, self).__init__()
+        self.kernel_size = kernel_size
+        self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)
+
+    def forward(self, x):
+        # padding on the both ends of time series
+        front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)
+        end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
+        x = torch.cat([front, x, end], dim=1)
+        x = self.avg(x.permute(0, 2, 1))
+        x = x.permute(0, 2, 1)
+        return x
+
+
+class series_decomp(nn.Module):
+    """
+    Series decomposition block
+    """
+    def __init__(self, kernel_size):
+        super(series_decomp, self).__init__()
+        self.moving_avg = moving_avg(kernel_size, stride=1)
+
+    def forward(self, x):
+        moving_mean = self.moving_avg(x)
+        res = x - moving_mean
+        return res, moving_mean
+    
+    
+    
+# pos_encoding
+
+def PositionalEncoding(q_len, d_model, normalize=True):
+    pe = torch.zeros(q_len, d_model)
+    position = torch.arange(0, q_len).unsqueeze(1)
+    div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model))
+    pe[:, 0::2] = torch.sin(position * div_term)
+    pe[:, 1::2] = torch.cos(position * div_term)
+    if normalize:
+        pe = pe - pe.mean()
+        pe = pe / (pe.std() * 10)
+    return pe
+
+SinCosPosEncoding = PositionalEncoding
+
+def Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True, eps=1e-3, verbose=False):
+    x = .5 if exponential else 1
+    i = 0
+    for i in range(100):
+        cpe = 2 * (torch.linspace(0, 1, q_len).reshape(-1, 1) ** x) * (torch.linspace(0, 1, d_model).reshape(1, -1) ** x) - 1
+        pv(f'{i:4.0f}  {x:5.3f}  {cpe.mean():+6.3f}', verbose)
+        if abs(cpe.mean()) <= eps: break
+        elif cpe.mean() > eps: x += .001
+        else: x -= .001
+        i += 1
+    if normalize:
+        cpe = cpe - cpe.mean()
+        cpe = cpe / (cpe.std() * 10)
+    return cpe
+
+def Coord1dPosEncoding(q_len, exponential=False, normalize=True):
+    cpe = (2 * (torch.linspace(0, 1, q_len).reshape(-1, 1)**(.5 if exponential else 1)) - 1)
+    if normalize:
+        cpe = cpe - cpe.mean()
+        cpe = cpe / (cpe.std() * 10)
+    return cpe
+
+def positional_encoding(pe, learn_pe, q_len, d_model):
+    # Positional encoding
+    if pe == None:
+        W_pos = torch.empty((q_len, d_model)) # pe = None and learn_pe = False can be used to measure impact of pe
+        nn.init.uniform_(W_pos, -0.02, 0.02)
+        learn_pe = False
+    elif pe == 'zero':
+        W_pos = torch.empty((q_len, 1))
+        nn.init.uniform_(W_pos, -0.02, 0.02)
+    elif pe == 'zeros':
+        W_pos = torch.empty((q_len, d_model))
+        nn.init.uniform_(W_pos, -0.02, 0.02)
+    elif pe == 'normal' or pe == 'gauss':
+        W_pos = torch.zeros((q_len, 1))
+        torch.nn.init.normal_(W_pos, mean=0.0, std=0.1)
+    elif pe == 'uniform':
+        W_pos = torch.zeros((q_len, 1))
+        nn.init.uniform_(W_pos, a=0.0, b=0.1)
+    elif pe == 'lin1d': W_pos = Coord1dPosEncoding(q_len, exponential=False, normalize=True)
+    elif pe == 'exp1d': W_pos = Coord1dPosEncoding(q_len, exponential=True, normalize=True)
+    elif pe == 'lin2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True)
+    elif pe == 'exp2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=True, normalize=True)
+    elif pe == 'sincos': W_pos = PositionalEncoding(q_len, d_model, normalize=True)
+    else: raise ValueError(f"{pe} is not a valid pe (positional encoder. Available types: 'gauss'=='normal', \
+        'zeros', 'zero', uniform', 'lin1d', 'exp1d', 'lin2d', 'exp2d', 'sincos', None.)")
+    return nn.Parameter(W_pos, requires_grad=learn_pe)
\ No newline at end of file
diff --git a/probts/model/nn/layers/RevIN.py b/probts/model/nn/layers/RevIN.py
new file mode 100644
index 0000000..f2f8285
--- /dev/null
+++ b/probts/model/nn/layers/RevIN.py
@@ -0,0 +1,69 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from RevIN
+# - Source: https://github.com/ts-kim/RevIN
+#
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+
+class RevIN(nn.Module):
+    def __init__(self, num_features: int, eps=1e-5, affine=True, subtract_last=False):
+        """
+        :param num_features: the number of features or channels
+        :param eps: a value added for numerical stability
+        :param affine: if True, RevIN has learnable affine parameters
+        """
+        super(RevIN, self).__init__()
+        self.num_features = num_features
+        self.eps = eps
+        self.affine = affine
+        self.subtract_last = subtract_last
+        if self.affine:
+            self._init_params()
+
+    def forward(self, x, mode:str):
+        if mode == 'norm':
+            self._get_statistics(x)
+            x = self._normalize(x)
+        elif mode == 'denorm':
+            x = self._denormalize(x)
+        else: raise NotImplementedError
+        return x
+
+    def _init_params(self):
+        # initialize RevIN params: (C,)
+        self.affine_weight = nn.Parameter(torch.ones(self.num_features))
+        self.affine_bias = nn.Parameter(torch.zeros(self.num_features))
+
+    def _get_statistics(self, x):
+        dim2reduce = tuple(range(1, x.ndim-1))
+        if self.subtract_last:
+            self.last = x[:,-1,:].unsqueeze(1)
+        else:
+            self.mean = torch.mean(x, dim=dim2reduce, keepdim=True).detach()
+        self.stdev = torch.sqrt(torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps).detach()
+
+    def _normalize(self, x):
+        if self.subtract_last:
+            x = x - self.last
+        else:
+            x = x - self.mean
+        x = x / self.stdev
+        if self.affine:
+            x = x * self.affine_weight
+            x = x + self.affine_bias
+        return x
+
+    def _denormalize(self, x):
+        if self.affine:
+            x = x - self.affine_bias
+            x = x / (self.affine_weight + self.eps*self.eps)
+        x = x * self.stdev
+        if self.subtract_last:
+            x = x + self.last
+        else:
+            x = x + self.mean
+        return x
diff --git a/probts/model/nn/layers/SelfAttention_Family.py b/probts/model/nn/layers/SelfAttention_Family.py
new file mode 100644
index 0000000..fc35f56
--- /dev/null
+++ b/probts/model/nn/layers/SelfAttention_Family.py
@@ -0,0 +1,331 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from math import sqrt
+from probts.utils.masking import TriangularCausalMask, ProbMask
+from reformer_pytorch import LSHSelfAttention
+from einops import rearrange
+
+
+# Code implementation from https://github.com/thuml/Flowformer
+class FlowAttention(nn.Module):
+    def __init__(self, attention_dropout=0.1):
+        super(FlowAttention, self).__init__()
+        self.dropout = nn.Dropout(attention_dropout)
+
+    def kernel_method(self, x):
+        return torch.sigmoid(x)
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        queries = queries.transpose(1, 2)
+        keys = keys.transpose(1, 2)
+        values = values.transpose(1, 2)
+        # kernel
+        queries = self.kernel_method(queries)
+        keys = self.kernel_method(keys)
+        # incoming and outgoing
+        normalizer_row = 1.0 / (torch.einsum("nhld,nhd->nhl", queries + 1e-6, keys.sum(dim=2) + 1e-6))
+        normalizer_col = 1.0 / (torch.einsum("nhsd,nhd->nhs", keys + 1e-6, queries.sum(dim=2) + 1e-6))
+        # reweighting
+        normalizer_row_refine = (
+            torch.einsum("nhld,nhd->nhl", queries + 1e-6, (keys * normalizer_col[:, :, :, None]).sum(dim=2) + 1e-6))
+        normalizer_col_refine = (
+            torch.einsum("nhsd,nhd->nhs", keys + 1e-6, (queries * normalizer_row[:, :, :, None]).sum(dim=2) + 1e-6))
+        # competition and allocation
+        normalizer_row_refine = torch.sigmoid(
+            normalizer_row_refine * (float(queries.shape[2]) / float(keys.shape[2])))
+        normalizer_col_refine = torch.softmax(normalizer_col_refine, dim=-1) * keys.shape[2]  # B h L vis
+        # multiply
+        kv = keys.transpose(-2, -1) @ (values * normalizer_col_refine[:, :, :, None])
+        x = (((queries @ kv) * normalizer_row[:, :, :, None]) * normalizer_row_refine[:, :, :, None]).transpose(1,
+                                                                                                                2).contiguous()
+        return x, None
+
+
+# Code implementation from https://github.com/shreyansh26/FlashAttention-PyTorch
+class FlashAttention(nn.Module):
+    def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
+        super(FlashAttention, self).__init__()
+        self.scale = scale
+        self.mask_flag = mask_flag
+        self.output_attention = output_attention
+        self.dropout = nn.Dropout(attention_dropout)
+
+    def flash_attention_forward(self, Q, K, V, mask=None):
+        BLOCK_SIZE = 32
+        NEG_INF = -1e10  # -infinity
+        EPSILON = 1e-10
+        # mask = torch.randint(0, 2, (128, 8)).to(device='cuda')
+        O = torch.zeros_like(Q, requires_grad=True)
+        l = torch.zeros(Q.shape[:-1])[..., None]
+        m = torch.ones(Q.shape[:-1])[..., None] * NEG_INF
+
+        O = O.to(device='cuda')
+        l = l.to(device='cuda')
+        m = m.to(device='cuda')
+
+        Q_BLOCK_SIZE = min(BLOCK_SIZE, Q.shape[-1])
+        KV_BLOCK_SIZE = BLOCK_SIZE
+
+        Q_BLOCKS = torch.split(Q, Q_BLOCK_SIZE, dim=2)
+        K_BLOCKS = torch.split(K, KV_BLOCK_SIZE, dim=2)
+        V_BLOCKS = torch.split(V, KV_BLOCK_SIZE, dim=2)
+        if mask is not None:
+            mask_BLOCKS = list(torch.split(mask, KV_BLOCK_SIZE, dim=1))
+
+        Tr = len(Q_BLOCKS)
+        Tc = len(K_BLOCKS)
+
+        O_BLOCKS = list(torch.split(O, Q_BLOCK_SIZE, dim=2))
+        l_BLOCKS = list(torch.split(l, Q_BLOCK_SIZE, dim=2))
+        m_BLOCKS = list(torch.split(m, Q_BLOCK_SIZE, dim=2))
+
+        for j in range(Tc):
+            Kj = K_BLOCKS[j]
+            Vj = V_BLOCKS[j]
+            if mask is not None:
+                maskj = mask_BLOCKS[j]
+
+            for i in range(Tr):
+                Qi = Q_BLOCKS[i]
+                Oi = O_BLOCKS[i]
+                li = l_BLOCKS[i]
+                mi = m_BLOCKS[i]
+
+                scale = 1 / np.sqrt(Q.shape[-1])
+                Qi_scaled = Qi * scale
+
+                S_ij = torch.einsum('... i d, ... j d -> ... i j', Qi_scaled, Kj)
+                if mask is not None:
+                    # Masking
+                    maskj_temp = rearrange(maskj, 'b j -> b 1 1 j')
+                    S_ij = torch.where(maskj_temp > 0, S_ij, NEG_INF)
+
+                m_block_ij, _ = torch.max(S_ij, dim=-1, keepdims=True)
+                P_ij = torch.exp(S_ij - m_block_ij)
+                if mask is not None:
+                    # Masking
+                    P_ij = torch.where(maskj_temp > 0, P_ij, 0.)
+
+                l_block_ij = torch.sum(P_ij, dim=-1, keepdims=True) + EPSILON
+
+                P_ij_Vj = torch.einsum('... i j, ... j d -> ... i d', P_ij, Vj)
+
+                mi_new = torch.maximum(m_block_ij, mi)
+                li_new = torch.exp(mi - mi_new) * li + torch.exp(m_block_ij - mi_new) * l_block_ij
+
+                O_BLOCKS[i] = (li / li_new) * torch.exp(mi - mi_new) * Oi + (
+                        torch.exp(m_block_ij - mi_new) / li_new) * P_ij_Vj
+                l_BLOCKS[i] = li_new
+                m_BLOCKS[i] = mi_new
+
+        O = torch.cat(O_BLOCKS, dim=2)
+        l = torch.cat(l_BLOCKS, dim=2)
+        m = torch.cat(m_BLOCKS, dim=2)
+        return O, l, m
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        res = \
+        self.flash_attention_forward(queries.permute(0, 2, 1, 3), keys.permute(0, 2, 1, 3), values.permute(0, 2, 1, 3),
+                                     attn_mask)[0]
+        return res.permute(0, 2, 1, 3).contiguous(), None
+
+
+class FullAttention(nn.Module):
+    def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
+        super(FullAttention, self).__init__()
+        self.scale = scale
+        self.mask_flag = mask_flag
+        self.output_attention = output_attention
+        self.dropout = nn.Dropout(attention_dropout)
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        B, L, H, E = queries.shape
+        _, S, _, D = values.shape
+        scale = self.scale or 1. / sqrt(E)
+
+        scores = torch.einsum("blhe,bshe->bhls", queries, keys)
+
+        if self.mask_flag:
+            if attn_mask is None:
+                attn_mask = TriangularCausalMask(B, L, device=queries.device)
+
+            scores.masked_fill_(attn_mask.mask, -np.inf)
+
+        A = self.dropout(torch.softmax(scale * scores, dim=-1))
+        V = torch.einsum("bhls,bshd->blhd", A, values)
+
+        if self.output_attention:
+            return (V.contiguous(), A)
+        else:
+            return (V.contiguous(), None)
+
+
+# Code implementation from https://github.com/zhouhaoyi/Informer2020
+class ProbAttention(nn.Module):
+    def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
+        super(ProbAttention, self).__init__()
+        self.factor = factor
+        self.scale = scale
+        self.mask_flag = mask_flag
+        self.output_attention = output_attention
+        self.dropout = nn.Dropout(attention_dropout)
+
+    def _prob_QK(self, Q, K, sample_k, n_top):  # n_top: c*ln(L_q)
+        # Q [B, H, L, D]
+        B, H, L_K, E = K.shape
+        _, _, L_Q, _ = Q.shape
+
+        # calculate the sampled Q_K
+        K_expand = K.unsqueeze(-3).expand(B, H, L_Q, L_K, E)
+        # real U = U_part(factor*ln(L_k))*L_q
+        index_sample = torch.randint(L_K, (L_Q, sample_k))
+        K_sample = K_expand[:, :, torch.arange(
+            L_Q).unsqueeze(1), index_sample, :]
+        Q_K_sample = torch.matmul(
+            Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze()
+
+        # find the Top_k query with sparisty measurement
+        M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K)
+        M_top = M.topk(n_top, sorted=False)[1]
+
+        # use the reduced Q to calculate Q_K
+        Q_reduce = Q[torch.arange(B)[:, None, None],
+                   torch.arange(H)[None, :, None],
+                   M_top, :]  # factor*ln(L_q)
+        Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1))  # factor*ln(L_q)*L_k
+
+        return Q_K, M_top
+
+    def _get_initial_context(self, V, L_Q):
+        B, H, L_V, D = V.shape
+        if not self.mask_flag:
+            # V_sum = V.sum(dim=-2)
+            V_sum = V.mean(dim=-2)
+            contex = V_sum.unsqueeze(-2).expand(B, H,
+                                                L_Q, V_sum.shape[-1]).clone()
+        else:  # use mask
+            # requires that L_Q == L_V, i.e. for self-attention only
+            assert (L_Q == L_V)
+            contex = V.cumsum(dim=-2)
+        return contex
+
+    def _update_context(self, context_in, V, scores, index, L_Q, attn_mask):
+        B, H, L_V, D = V.shape
+
+        if self.mask_flag:
+            attn_mask = ProbMask(B, H, L_Q, index, scores, device=V.device)
+            scores.masked_fill_(attn_mask.mask, -np.inf)
+
+        attn = torch.softmax(scores, dim=-1)  # nn.Softmax(dim=-1)(scores)
+
+        context_in[torch.arange(B)[:, None, None],
+        torch.arange(H)[None, :, None],
+        index, :] = torch.matmul(attn, V).type_as(context_in)
+        if self.output_attention:
+            attns = (torch.ones([B, H, L_V, L_V]) /
+                     L_V).type_as(attn).to(attn.device)
+            attns[torch.arange(B)[:, None, None], torch.arange(H)[
+                                                  None, :, None], index, :] = attn
+            return (context_in, attns)
+        else:
+            return (context_in, None)
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        B, L_Q, H, D = queries.shape
+        _, L_K, _, _ = keys.shape
+
+        queries = queries.transpose(2, 1)
+        keys = keys.transpose(2, 1)
+        values = values.transpose(2, 1)
+
+        U_part = self.factor * \
+                 np.ceil(np.log(L_K)).astype('int').item()  # c*ln(L_k)
+        u = self.factor * \
+            np.ceil(np.log(L_Q)).astype('int').item()  # c*ln(L_q)
+
+        U_part = U_part if U_part < L_K else L_K
+        u = u if u < L_Q else L_Q
+
+        scores_top, index = self._prob_QK(
+            queries, keys, sample_k=U_part, n_top=u)
+
+        # add scale factor
+        scale = self.scale or 1. / sqrt(D)
+        if scale is not None:
+            scores_top = scores_top * scale
+        # get the context
+        context = self._get_initial_context(values, L_Q)
+        # update the context with selected top_k queries
+        context, attn = self._update_context(
+            context, values, scores_top, index, L_Q, attn_mask)
+
+        return context.contiguous(), attn
+
+
+class AttentionLayer(nn.Module):
+    def __init__(self, attention, d_model, n_heads, d_keys=None,
+                 d_values=None):
+        super(AttentionLayer, self).__init__()
+
+        d_keys = d_keys or (d_model // n_heads)
+        d_values = d_values or (d_model // n_heads)
+
+        self.inner_attention = attention
+        self.query_projection = nn.Linear(d_model, d_keys * n_heads)
+        self.key_projection = nn.Linear(d_model, d_keys * n_heads)
+        self.value_projection = nn.Linear(d_model, d_values * n_heads)
+        self.out_projection = nn.Linear(d_values * n_heads, d_model)
+        self.n_heads = n_heads
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        B, L, _ = queries.shape
+        _, S, _ = keys.shape
+        H = self.n_heads
+
+        queries = self.query_projection(queries).view(B, L, H, -1)
+        keys = self.key_projection(keys).view(B, S, H, -1)
+        values = self.value_projection(values).view(B, S, H, -1)
+
+        out, attn = self.inner_attention(
+            queries,
+            keys,
+            values,
+            attn_mask,
+            tau=tau,
+            delta=delta
+        )
+        out = out.view(B, L, -1)
+
+        return self.out_projection(out), attn
+
+
+class ReformerLayer(nn.Module):
+    def __init__(self, attention, d_model, n_heads, d_keys=None,
+                 d_values=None, causal=False, bucket_size=4, n_hashes=4):
+        super().__init__()
+        self.bucket_size = bucket_size
+        self.attn = LSHSelfAttention(
+            dim=d_model,
+            heads=n_heads,
+            bucket_size=bucket_size,
+            n_hashes=n_hashes,
+            causal=causal
+        )
+
+    def fit_length(self, queries):
+        # inside reformer: assert N % (bucket_size * 2) == 0
+        B, N, C = queries.shape
+        if N % (self.bucket_size * 2) == 0:
+            return queries
+        else:
+            # fill the time series
+            fill_len = (self.bucket_size * 2) - (N % (self.bucket_size * 2))
+            return torch.cat([queries, torch.zeros([B, fill_len, C]).to(queries.device)], dim=1)
+
+    def forward(self, queries, keys, values, attn_mask, tau, delta):
+        # in Reformer: defalut queries=keys
+        B, N, C = queries.shape
+        queries = self.attn(self.fit_length(queries))[:, :N, :]
+        return queries, None
+
diff --git a/probts/model/nn/layers/Transformer_EncDec.py b/probts/model/nn/layers/Transformer_EncDec.py
new file mode 100644
index 0000000..c48ddc3
--- /dev/null
+++ b/probts/model/nn/layers/Transformer_EncDec.py
@@ -0,0 +1,134 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ConvLayer(nn.Module):
+    def __init__(self, c_in):
+        super(ConvLayer, self).__init__()
+        self.downConv = nn.Conv1d(in_channels=c_in,
+                                  out_channels=c_in,
+                                  kernel_size=3,
+                                  padding=2,
+                                  padding_mode='circular')
+        self.norm = nn.BatchNorm1d(c_in)
+        self.activation = nn.ELU()
+        self.maxPool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
+
+    def forward(self, x):
+        x = self.downConv(x.permute(0, 2, 1))
+        x = self.norm(x)
+        x = self.activation(x)
+        x = self.maxPool(x)
+        x = x.transpose(1, 2)
+        return x
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"):
+        super(EncoderLayer, self).__init__()
+        d_ff = d_ff or 4 * d_model
+        self.attention = attention
+        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
+        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, attn_mask=None, tau=None, delta=None):
+        new_x, attn = self.attention(
+            x, x, x,
+            attn_mask=attn_mask,
+            tau=tau, delta=delta
+        )
+        x = x + self.dropout(new_x)
+
+        y = x = self.norm1(x)
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
+        y = self.dropout(self.conv2(y).transpose(-1, 1))
+
+        return self.norm2(x + y), attn
+
+
+class Encoder(nn.Module):
+    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
+        super(Encoder, self).__init__()
+        self.attn_layers = nn.ModuleList(attn_layers)
+        self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
+        self.norm = norm_layer
+
+    def forward(self, x, attn_mask=None, tau=None, delta=None):
+        # x [B, L, D]
+        attns = []
+        if self.conv_layers is not None:
+            for i, (attn_layer, conv_layer) in enumerate(zip(self.attn_layers, self.conv_layers)):
+                delta = delta if i == 0 else None
+                x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
+                x = conv_layer(x)
+                attns.append(attn)
+            x, attn = self.attn_layers[-1](x, tau=tau, delta=None)
+            attns.append(attn)
+        else:
+            for attn_layer in self.attn_layers:
+                x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
+                attns.append(attn)
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x, attns
+
+
+class DecoderLayer(nn.Module):
+    def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
+                 dropout=0.1, activation="relu"):
+        super(DecoderLayer, self).__init__()
+        d_ff = d_ff or 4 * d_model
+        self.self_attention = self_attention
+        self.cross_attention = cross_attention
+        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
+        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
+        x = x + self.dropout(self.self_attention(
+            x, x, x,
+            attn_mask=x_mask,
+            tau=tau, delta=None
+        )[0])
+        x = self.norm1(x)
+
+        x = x + self.dropout(self.cross_attention(
+            x, cross, cross,
+            attn_mask=cross_mask,
+            tau=tau, delta=delta
+        )[0])
+
+        y = x = self.norm2(x)
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
+        y = self.dropout(self.conv2(y).transpose(-1, 1))
+
+        return self.norm3(x + y)
+
+
+class Decoder(nn.Module):
+    def __init__(self, layers, norm_layer=None, projection=None):
+        super(Decoder, self).__init__()
+        self.layers = nn.ModuleList(layers)
+        self.norm = norm_layer
+        self.projection = projection
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
+        for layer in self.layers:
+            x = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask, tau=tau, delta=delta)
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        if self.projection is not None:
+            x = self.projection(x)
+        return x
diff --git a/probts/model/nn/layers/__init__.py b/probts/model/nn/layers/__init__.py
new file mode 100644
index 0000000..4a63681
--- /dev/null
+++ b/probts/model/nn/layers/__init__.py
@@ -0,0 +1,5 @@
+from .PatchTST_backbone import *
+from .PatchTST_layers import *
+from .RevIN import *
+from .diffusion_layers import *
+# from .Moirai_backbone import *
\ No newline at end of file
diff --git a/probts/model/nn/layers/diffusion_layers.py b/probts/model/nn/layers/diffusion_layers.py
new file mode 100644
index 0000000..82eb090
--- /dev/null
+++ b/probts/model/nn/layers/diffusion_layers.py
@@ -0,0 +1,173 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PyTorch-TS
+# - Source: https://github.com/zalandoresearch/pytorch-ts
+# - Paper: Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
+# - License: MIT, Apache-2.0 license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from linear_attention_transformer import LinearAttentionTransformer
+
+def get_torch_trans(heads=8, layers=1, channels=64,linear=False):
+    if linear:
+        encoder_layer = LinearAttentionTransformer(
+            dim = channels,
+            heads = heads,
+            depth = layers,
+            max_seq_len = 4096,
+            n_local_attn_heads = 0
+        )
+        return encoder_layer
+    else:
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=channels, nhead=heads, dim_feedforward=64, activation="gelu"
+        )
+        return nn.TransformerEncoder(encoder_layer, num_layers=layers)
+
+
+def Conv1d_with_init(in_channels, out_channels, kernel_size):
+    layer = nn.Conv1d(in_channels, out_channels, kernel_size)
+    nn.init.kaiming_normal_(layer.weight)
+    return layer
+
+
+class DiffusionEmbedding(nn.Module):
+    def __init__(self, dim=128, proj_dim=None, max_steps=500):
+        super().__init__()
+        if proj_dim is None:
+            proj_dim = dim
+        self.register_buffer(
+            "embedding", self._build_embedding(dim, max_steps), persistent=False
+        )
+        self.projection1 = nn.Linear(dim * 2, proj_dim)
+        self.projection2 = nn.Linear(proj_dim, proj_dim)
+
+    def forward(self, diffusion_step):
+        x = self.embedding[diffusion_step]
+        x = self.projection1(x)
+        x = F.silu(x)
+        x = self.projection2(x)
+        x = F.silu(x)
+        return x
+
+    def _build_embedding(self, dim, max_steps):
+        steps = torch.arange(max_steps).unsqueeze(1)  # [T,1]
+        dims = torch.arange(dim).unsqueeze(0)  # [1,dim]
+        table = steps * 10.0 ** (dims * 4.0 / dim)  # [T,dim]
+        table = torch.cat([torch.sin(table), torch.cos(table)], dim=1)
+        return table
+
+
+class diff_CSDI(nn.Module):
+    def __init__(self, channels, diffusion_embedding_dim, side_dim, num_steps, nheads, n_layers, inputdim=2, linear=False):
+        super().__init__()
+        self.channels = channels
+
+        self.diffusion_embedding = DiffusionEmbedding(
+            dim=diffusion_embedding_dim, max_steps=num_steps
+        )
+        self.input_projection = Conv1d_with_init(inputdim, self.channels, 1)
+        self.output_projection1 = Conv1d_with_init(self.channels, self.channels, 1)
+        self.output_projection2 = Conv1d_with_init(self.channels, 1, 1)
+        nn.init.zeros_(self.output_projection2.weight)
+
+        self.residual_layers = nn.ModuleList(
+            [
+                ResidualBlock(
+                    side_dim=side_dim,
+                    channels=self.channels,
+                    diffusion_embedding_dim=diffusion_embedding_dim,
+                    nheads=nheads,
+                    linear=linear,
+                )
+                for _ in range(n_layers)
+            ]
+        )
+
+    def forward(self, x, cond_info, diffusion_step):
+        B, inputdim, K, L = x.shape
+
+        x = x.reshape(B, inputdim, K * L)
+
+        x = self.input_projection(x)
+        x = F.relu(x)
+        x = x.reshape(B, self.channels, K, L)
+
+        diffusion_emb = self.diffusion_embedding(diffusion_step)
+
+        skip = []
+        for layer in self.residual_layers:
+            x, skip_connection = layer(x, cond_info, diffusion_emb)
+            skip.append(skip_connection)
+
+        x = torch.sum(torch.stack(skip), dim=0) / math.sqrt(len(self.residual_layers))
+        x = x.reshape(B, self.channels, K * L)
+        x = self.output_projection1(x)  # (B,channel,K*L)
+        x = F.relu(x)
+        x = self.output_projection2(x)  # (B,1,K*L)
+        x = x.reshape(B, K, L)
+        return x
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, side_dim, channels, diffusion_embedding_dim, nheads, linear=False):
+        super().__init__()
+        self.side_dim = side_dim
+        self.diffusion_projection = nn.Linear(diffusion_embedding_dim, channels)
+        self.cond_projection = Conv1d_with_init(side_dim, 2 * channels, 1)
+        self.mid_projection = Conv1d_with_init(channels, 2 * channels, 1)
+        self.output_projection = Conv1d_with_init(channels, 2 * channels, 1)
+
+        self.time_layer = get_torch_trans(heads=nheads, layers=1, channels=channels,linear=linear)
+        self.feature_layer = get_torch_trans(heads=nheads, layers=1, channels=channels,linear=linear)
+
+    def forward_time(self, y, base_shape):
+        B, channel, K, L = base_shape
+        if L == 1:
+            return y
+        y = y.reshape(B, channel, K, L).permute(0, 2, 1, 3).reshape(B * K, channel, L)
+        y = self.time_layer(y.permute(2, 0, 1)).permute(1, 2, 0)
+        y = y.reshape(B, K, channel, L).permute(0, 2, 1, 3).reshape(B, channel, K * L)
+        return y
+
+    def forward_feature(self, y, base_shape):
+        B, channel, K, L = base_shape
+        if K == 1:
+            return y
+        y = y.reshape(B, channel, K, L).permute(0, 3, 1, 2).reshape(B * L, channel, K)
+        y = self.feature_layer(y.permute(2, 0, 1)).permute(1, 2, 0)
+        y = y.reshape(B, L, channel, K).permute(0, 2, 3, 1).reshape(B, channel, K * L)
+        return y
+
+    def forward(self, x, cond_info, diffusion_emb):
+
+        B, channel, K, L = x.shape
+        base_shape = x.shape
+        x = x.reshape(B, channel, K * L)
+
+        diffusion_emb = self.diffusion_projection(diffusion_emb).unsqueeze(-1)  # (B,channel,1)
+        y = x + diffusion_emb
+
+        y = self.forward_time(y, base_shape)
+        y = self.forward_feature(y, base_shape)  # (B,channel,K*L)
+        y = self.mid_projection(y)  # (B,2*channel,K*L)
+        _, cond_dim, _, _ = cond_info.shape
+        cond_info = cond_info.reshape(B, cond_dim, K * L)
+        cond_info = self.cond_projection(cond_info)  # (B,2*channel,K*L)
+        y = y + cond_info
+
+        gate, filter = torch.chunk(y, 2, dim=1)
+        y = torch.sigmoid(gate) * torch.tanh(filter)  # (B,channel,K*L)
+        y = self.output_projection(y)
+
+        residual, skip = torch.chunk(y, 2, dim=1)
+        x = x.reshape(base_shape)
+        residual = residual.reshape(base_shape)
+        skip = skip.reshape(base_shape)
+        return (x + residual) / math.sqrt(2.0), skip
diff --git a/probts/model/nn/layers/s4.py b/probts/model/nn/layers/s4.py
new file mode 100644
index 0000000..b9ca6ee
--- /dev/null
+++ b/probts/model/nn/layers/s4.py
@@ -0,0 +1,1840 @@
+"""Standalone version of Structured (Sequence) State Space (S4) model."""
+
+import logging
+from functools import partial
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from pytorch_lightning.utilities import rank_zero_only
+from einops import rearrange, repeat
+import opt_einsum as oe
+
+contract = oe.contract
+contract_expression = oe.contract_expression
+
+
+def get_logger(name=__name__, level=logging.INFO) -> logging.Logger:
+    """Initializes multi-GPU-friendly python logger."""
+
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+
+    # this ensures all logging levels get marked with the rank zero decorator
+    # otherwise logs would get multiplied for each GPU process in multi-GPU setup
+    for level in (
+        "debug",
+        "info",
+        "warning",
+        "error",
+        "exception",
+        "fatal",
+        "critical",
+    ):
+        setattr(logger, level, rank_zero_only(getattr(logger, level)))
+
+    return logger
+
+
+log = get_logger(__name__)
+
+""" Cauchy and Vandermonde kernels """
+
+try:  # Try CUDA extension
+    from extensions.cauchy.cauchy import cauchy_mult
+
+    has_cauchy_extension = True
+except ImportError:
+    # log.warning(
+    #     "CUDA extension for cauchy multiplication not found. Install by going to extensions/cauchy/ and running `python setup.py install`. This should speed up end-to-end training by 10-50%"
+    # )
+    has_cauchy_extension = False
+
+try:  # Try pykeops
+    from pykeops.torch import Genred
+
+    has_pykeops = True
+    # log.info("Pykeops installation found.")
+
+    def _broadcast_dims(*tensors):
+        max_dim = max([len(tensor.shape) for tensor in tensors])
+        tensors = [
+            tensor.view((1,) * (max_dim - len(tensor.shape)) + tensor.shape)
+            for tensor in tensors
+        ]
+        return tensors
+
+    def cauchy_conj(v, z, w):
+        """Pykeops version"""
+        expr_num = "z * ComplexReal(v) - Real2Complex(Sum(v * w))"
+        expr_denom = "ComplexMult(z-w, z-Conj(w))"
+
+        cauchy_mult = Genred(
+            f"ComplexDivide({expr_num}, {expr_denom})",
+            [
+                "v = Vj(2)",
+                "z = Vi(2)",
+                "w = Vj(2)",
+            ],
+            reduction_op="Sum",
+            axis=1,
+        )
+
+        v, z, w = _broadcast_dims(v, z, w)
+        v = _c2r(v)
+        z = _c2r(z)
+        w = _c2r(w)
+
+        r = 2 * cauchy_mult(v, z, w, backend="GPU")
+        return _r2c(r)
+
+    def log_vandermonde(v, x, L):
+        expr = "ComplexMult(v, ComplexExp(ComplexMult(x, l)))"
+        vandermonde_mult = Genred(
+            expr,
+            [
+                "v = Vj(2)",
+                "x = Vj(2)",
+                "l = Vi(2)",
+            ],
+            reduction_op="Sum",
+            axis=1,
+        )
+
+        l = torch.arange(L).to(x)
+        v, x, l = _broadcast_dims(v, x, l)
+        v = _c2r(v)
+        x = _c2r(x)
+        l = _c2r(l)
+
+        r = vandermonde_mult(v, x, l, backend="GPU")
+        return 2 * _r2c(r).real
+
+    def log_vandermonde_transpose(u, v, x, L):
+        """
+        u: ... H L
+        v: ... H N
+        x: ... H N
+        Returns: ... H N
+
+        V = Vandermonde(a, L) : (H N L)
+        contract_L(V * u * v)
+        """
+        expr = "ComplexMult(ComplexMult(v, u), ComplexExp(ComplexMult(x, l)))"
+        vandermonde_mult = Genred(
+            expr,
+            [
+                "u = Vj(2)",
+                "v = Vi(2)",
+                "x = Vi(2)",
+                "l = Vj(2)",
+            ],
+            reduction_op="Sum",
+            axis=1,
+        )
+
+        l = torch.arange(L).to(x)
+        u, v, x, l = _broadcast_dims(u, v, x, l)
+        u = _c2r(u)
+        v = _c2r(v)
+        x = _c2r(x)
+        l = _c2r(l)
+
+        r = vandermonde_mult(u, v, x, l, backend="GPU")
+        return _r2c(r)
+
+except ImportError:
+    has_pykeops = False
+    if not has_cauchy_extension:
+        log.warning(
+            "Falling back on slow Cauchy kernel. Install at least one of pykeops or the CUDA extension for efficiency."
+        )
+
+        def cauchy_naive(v, z, w):
+            """
+            v, w: (..., N)
+            z: (..., L)
+            returns: (..., L)
+            """
+            cauchy_matrix = v.unsqueeze(-1) / (
+                z.unsqueeze(-2) - w.unsqueeze(-1)
+            )  # (... N L)
+            return torch.sum(cauchy_matrix, dim=-2)
+
+    # Vandermonde functions
+    log.warning(
+        "Falling back on slow Vandermonde kernel. Install pykeops for improved memory efficiency."
+    )
+
+    def log_vandermonde(v, x, L):
+        """
+        v: (..., N)
+        x: (..., N)
+        returns: (..., L) \sum v x^l
+        """
+        vandermonde_matrix = torch.exp(
+            x.unsqueeze(-1) * torch.arange(L).to(x)
+        )  # (... N L)
+        vandermonde_prod = contract(
+            "... n, ... n l -> ... l", v, vandermonde_matrix
+        )  # (... L)
+        return 2 * vandermonde_prod.real
+
+    def log_vandermonde_transpose(u, v, x, L):
+        vandermonde_matrix = torch.exp(
+            x.unsqueeze(-1) * torch.arange(L).to(x)
+        )  # (... N L)
+        vandermonde_prod = contract(
+            "... l, ... n, ... n l -> ... n",
+            u.to(x),
+            v.to(x),
+            vandermonde_matrix,
+        )  # (... L)
+        return vandermonde_prod
+
+
+def _conj(x):
+    return torch.cat([x, x.conj()], dim=-1)
+
+
+_c2r = torch.view_as_real
+_r2c = torch.view_as_complex
+if tuple(map(int, torch.__version__.split(".")[:2])) >= (1, 10):
+
+    def _resolve_conj(x):
+        return x.conj().resolve_conj()
+
+else:
+
+    def _resolve_conj(x):
+        return x.conj()
+
+
+""" Simple nn.Module components """
+
+
+def Activation(activation=None, dim=-1):
+    if activation in [None, "id", "identity", "linear"]:
+        return nn.Identity()
+    elif activation == "tanh":
+        return nn.Tanh()
+    elif activation == "relu":
+        return nn.ReLU()
+    elif activation == "gelu":
+        return nn.GELU()
+    elif activation in ["swish", "silu"]:
+        return nn.SiLU()
+    elif activation == "glu":
+        return nn.GLU(dim=dim)
+    elif activation == "sigmoid":
+        return nn.Sigmoid()
+    else:
+        raise NotImplementedError(
+            "hidden activation '{}' is not implemented".format(activation)
+        )
+
+
+def LinearActivation(
+    d_input,
+    d_output,
+    bias=True,
+    transposed=False,
+    activation=None,
+    activate=False,  # Apply activation as part of this module
+    **kwargs,
+):
+    """Returns a linear nn.Module with control over axes order, initialization, and activation"""
+
+    # Construct core module
+    linear_cls = partial(nn.Conv1d, kernel_size=1) if transposed else nn.Linear
+    if activation == "glu":
+        d_output *= 2
+    linear = linear_cls(d_input, d_output, bias=bias, **kwargs)
+
+    if activate and activation is not None:
+        activation = Activation(activation, dim=-2 if transposed else -1)
+        linear = nn.Sequential(linear, activation)
+    return linear
+
+
+class DropoutNd(nn.Module):
+    def __init__(self, p: float = 0.5, tie=True, transposed=True):
+        """
+        tie: tie dropout mask across sequence lengths (Dropout1d/2d/3d)
+        """
+        super().__init__()
+        if p < 0 or p >= 1:
+            raise ValueError(
+                "dropout probability has to be in [0, 1), "
+                "but got {}".format(p)
+            )
+        self.p = p
+        self.tie = tie
+        self.transposed = transposed
+        self.binomial = torch.distributions.binomial.Binomial(probs=1 - self.p)
+
+    def forward(self, X):
+        """X: (batch, dim, lengths...)"""
+        if self.training:
+            if not self.transposed:
+                X = rearrange(X, "b d ... -> b ... d")
+            mask_shape = (
+                X.shape[:2] + (1,) * (X.ndim - 2) if self.tie else X.shape
+            )
+            mask = torch.rand(*mask_shape, device=X.device) < 1.0 - self.p
+            X = X * mask * (1.0 / (1 - self.p))
+            if not self.transposed:
+                X = rearrange(X, "b ... d -> b d ...")
+            return X
+        return X
+
+
+""" Misc functional utilities """
+
+
+def power(L, A, v=None):
+    """Compute A^L and the scan sum_i A^i v_i
+
+    A: (..., N, N)
+    v: (..., N, L)
+    """
+
+    I = torch.eye(A.shape[-1]).to(A)  # , dtype=A.dtype, device=A.device)
+
+    powers = [A]
+    l = 1
+    while True:
+        if L % 2 == 1:
+            I = powers[-1] @ I
+        L //= 2
+        if L == 0:
+            break
+        l *= 2
+        powers.append(powers[-1] @ powers[-1])
+
+    if v is None:
+        return I
+
+    # Invariants:
+    # powers[-1] := A^l
+    # l := largest po2 at most L
+
+    # Note that an alternative divide and conquer to compute the reduction is possible and can be embedded into the above loop without caching intermediate powers of A
+    # We do this reverse divide-and-conquer for efficiency reasons:
+    # 1) it involves fewer padding steps for non-po2 L
+    # 2) it involves more contiguous arrays
+
+    # Take care of edge case for non-po2 arrays
+    # Note that this initial step is a no-op for the case of power of 2 (l == L)
+    k = v.size(-1) - l
+    v_ = powers.pop() @ v[..., l:]
+    v = v[..., :l]
+    v[..., :k] = v[..., :k] + v_
+
+    # Handle reduction for power of 2
+    while v.size(-1) > 1:
+        v = rearrange(v, "... (z l) -> ... z l", z=2)
+        v = v[..., 0, :] + powers.pop() @ v[..., 1, :]
+    return I, v.squeeze(-1)
+
+
+""" HiPPO utilities """
+
+
+def transition(measure, N):
+    """A, B transition matrices for different measures"""
+    # Legendre (translated)
+    if measure == "legt":
+        Q = np.arange(N, dtype=np.float64)
+        R = (2 * Q + 1) ** 0.5
+        j, i = np.meshgrid(Q, Q)
+        A = R[:, None] * np.where(i < j, (-1.0) ** (i - j), 1) * R[None, :]
+        B = R[:, None]
+        A = -A
+
+        # Halve again for timescale correctness
+        A *= 0.5
+        B *= 0.5
+    # Legendre (scaled)
+    elif measure == "legs":
+        q = np.arange(N, dtype=np.float64)
+        col, row = np.meshgrid(q, q)
+        r = 2 * q + 1
+        M = -(np.where(row >= col, r, 0) - np.diag(q))
+        T = np.sqrt(np.diag(2 * q + 1))
+        A = T @ M @ np.linalg.inv(T)
+        B = np.diag(T)[:, None]
+        B = (
+            B.copy()
+        )  # Otherwise "UserWarning: given NumPY array is not writeable..." after torch.as_tensor(B)
+    elif measure == "legsd":
+        # Essentially equivalent to S4D-LegS
+        q = np.arange(N, dtype=np.float64)
+        col, row = np.meshgrid(q, q)
+        r = 2 * q + 1
+        M = -(np.where(row >= col, r, 0) - np.diag(q))
+        T = np.sqrt(np.diag(2 * q + 1))
+        A = T @ M @ np.linalg.inv(T)
+        B = np.diag(T)[:, None]
+        B = (
+            B.copy()
+        )  # Otherwise "UserWarning: given NumPY array is not writeable..." after torch.as_tensor(B)
+        A += 0.5 * B * B[None, :, 0]
+        B = B / 2.0
+    elif measure in ["fourier_diag", "foud"]:
+        # Essentially equivalent to S4D-Lin
+        freqs = np.arange(N // 2)
+        d = np.stack([freqs, np.zeros(N // 2)], axis=-1).reshape(-1)[:-1]
+        A = 2 * np.pi * (-np.diag(d, 1) + np.diag(d, -1))
+        A = A - 0.5 * np.eye(N)
+        B = np.zeros(N)
+        B[0::2] = 2**0.5
+        B[0] = 1
+        B = B[:, None]
+    elif measure in ["fourier", "fout"]:
+        freqs = np.arange(N // 2)
+        d = np.stack([np.zeros(N // 2), freqs], axis=-1).reshape(-1)[1:]
+        A = np.pi * (-np.diag(d, 1) + np.diag(d, -1))
+        B = np.zeros(N)
+        B[0::2] = 2**0.5
+        B[0] = 1
+
+        # Subtract off rank correction - this corresponds to the other endpoint u(t-1) in this case
+        A = A - B[:, None] * B[None, :]
+        B = B[:, None]
+    else:
+        raise NotImplementedError
+
+    return A, B
+
+
+def rank_correction(measure, N, rank=1, dtype=torch.float):
+    """Return low-rank matrix L such that A + L is normal"""
+
+    if measure == "legs":
+        assert rank >= 1
+        P = torch.sqrt(0.5 + torch.arange(N, dtype=dtype)).unsqueeze(
+            0
+        )  # (1 N)
+    elif measure == "legt":
+        assert rank >= 2
+        P = torch.sqrt(1 + 2 * torch.arange(N, dtype=dtype))  # (N)
+        P0 = P.clone()
+        P0[0::2] = 0.0
+        P1 = P.clone()
+        P1[1::2] = 0.0
+        P = torch.stack([P0, P1], dim=0)  # (2 N)
+        P *= 2 ** (
+            -0.5
+        )  # Halve the rank correct just like the original matrix was halved
+    elif measure in ["fourier", "fout"]:
+        P = torch.zeros(N)
+        P[0::2] = 2**0.5
+        P[0] = 1
+        P = P.unsqueeze(0)
+    elif measure in ["fourier_diag", "foud", "legsd"]:
+        P = torch.zeros(1, N, dtype=dtype)
+    else:
+        raise NotImplementedError
+
+    d = P.size(0)
+    if rank > d:
+        P = torch.cat(
+            [P, torch.zeros(rank - d, N, dtype=dtype)], dim=0
+        )  # (rank N)
+    return P
+
+
+def nplr(measure, N, rank=1, dtype=torch.float, diagonalize_precision=True):
+    """Return w, p, q, V, B such that
+    (w - p q^*, B) is unitarily equivalent to the original HiPPO A, B by the matrix V
+    i.e. A = V[w - p q^*]V^*, B = V B
+    """
+    assert dtype == torch.float or dtype == torch.double
+    cdtype = torch.cfloat if dtype == torch.float else torch.cdouble
+
+    A, B = transition(measure, N)
+    A = torch.as_tensor(A, dtype=dtype)  # (N, N)
+    B = torch.as_tensor(B, dtype=dtype)[:, 0]  # (N,)
+
+    P = rank_correction(measure, N, rank=rank, dtype=dtype)  # (r N)
+    AP = A + torch.sum(P.unsqueeze(-2) * P.unsqueeze(-1), dim=-3)
+
+    # We require AP to be nearly skew-symmetric
+    _A = AP + AP.transpose(-1, -2)
+    if (
+        err := torch.sum((_A - _A[0, 0] * torch.eye(N)) ** 2) / N
+    ) > 1e-5:  # if not torch.allclose(_A - _A[0,0]*torch.eye(N), torch.zeros(N, N), atol=1e-5):
+        print("WARNING: HiPPO matrix not skew symmetric", err)
+
+    # Take advantage of identity + skew-symmetric form to calculate real and imaginary parts separately
+    # Imaginary part can use eigh instead of eig
+    w_re = torch.mean(torch.diagonal(AP), -1, keepdim=True)
+
+    # Diagonalize in double precision
+    if diagonalize_precision:
+        AP = AP.to(torch.double)
+    w_im, V = torch.linalg.eigh(AP * -1j)  # (..., N) (..., N, N)
+    if diagonalize_precision:
+        w_im, V = w_im.to(cdtype), V.to(cdtype)
+    w = w_re + 1j * w_im
+    # Check: V w V^{-1} = A
+    # print("check", V @ torch.diag_embed(w) @ V.conj().transpose(-1, -2))
+
+    # Only keep half of each conjugate pair
+    _, idx = torch.sort(w.imag)
+    w_sorted = w[idx]
+    V_sorted = V[:, idx]
+
+    # There is an edge case when eigenvalues can be 0, which requires some machinery to handle
+    # We use a huge hack here: Assume only one pair is 0, and that it is the first row/column of A (only happens in Fourier case)
+    V = V_sorted[:, : N // 2]
+    w = w_sorted[: N // 2]
+    assert (
+        w[-2].abs() > 1e-4
+    ), "Only 1 zero eigenvalue allowed in diagonal part of A"
+    if w[-1].abs() < 1e-4:
+        V[:, -1] = 0.0
+        V[0, -1] = 2**-0.5
+        V[1, -1] = 2**-0.5 * 1j
+
+    _AP = V @ torch.diag_embed(w) @ V.conj().transpose(-1, -2)
+    if (err := torch.sum((2 * _AP.real - AP) ** 2) / N) > 1e-5:
+        print(
+            "Warning: Diagonalization of A matrix not numerically precise - error",
+            err,
+        )
+    # print("check", V @ torch.diag_embed(w) @ V.conj().transpose(-1, -2))
+
+    V_inv = V.conj().transpose(-1, -2)
+
+    B = contract("ij, j -> i", V_inv, B.to(V))  # V^* B
+    P = contract("ij, ...j -> ...i", V_inv, P.to(V))  # V^* P
+
+    return w, P, B, V
+
+
+def dplr(
+    scaling,
+    N,
+    rank=1,
+    H=1,
+    dtype=torch.float,
+    real_scale=1.0,
+    imag_scale=1.0,
+    random_real=False,
+    random_imag=False,
+    normalize=False,
+    diagonal=True,
+    random_B=False,
+):
+    assert dtype == torch.float or dtype == torch.double
+    dtype = torch.cfloat if dtype == torch.float else torch.cdouble
+
+    pi = torch.tensor(math.pi)
+    if random_real:
+        real_part = torch.rand(H, N // 2)
+    else:
+        real_part = 0.5 * torch.ones(H, N // 2)
+    if random_imag:
+        imag_part = N // 2 * torch.rand(H, N // 2)
+    else:
+        imag_part = repeat(torch.arange(N // 2), "n -> h n", h=H)
+
+    real_part = real_scale * real_part
+    if scaling == "random":
+        imag_part = torch.randn(H, N // 2)
+    elif scaling == "real":
+        imag_part = 0 * imag_part
+        real_part = 1 + repeat(torch.arange(N // 2), "n -> h n", h=H)
+    elif scaling in ["linear", "lin"]:
+        imag_part = pi * imag_part
+    elif scaling in [
+        "inverse",
+        "inv",
+    ]:  # Based on asymptotics of the default HiPPO matrix
+        imag_part = 1 / pi * N * (N / (1 + 2 * imag_part) - 1)
+    elif scaling in ["inverse2", "inv2"]:
+        imag_part = 1 / pi * N * (N / (1 + imag_part) - 1)
+    elif scaling in ["quadratic", "quad"]:
+        imag_part = 1 / pi * (1 + 2 * imag_part) ** 2
+    elif scaling in ["legs", "hippo"]:
+        w, _, _, _ = nplr("legsd", N)
+        imag_part = w.imag
+
+    else:
+        raise NotImplementedError
+    imag_part = imag_scale * imag_part
+    w = -real_part + 1j * imag_part
+
+    # Initialize B
+    if random_B:
+        B = torch.randn(H, N // 2, dtype=dtype)
+    else:
+        B = torch.ones(H, N // 2, dtype=dtype)
+
+    if normalize:
+        norm = (
+            -B / w
+        )  # (H, N) # Result if you integrate the kernel with constant 1 function
+        zeta = 2 * torch.sum(
+            torch.abs(norm) ** 2, dim=-1, keepdim=True
+        )  # Variance with a random C vector
+        B = B / zeta**0.5
+
+    P = torch.randn(rank, H, N // 2, dtype=dtype)
+    if diagonal:
+        P = P * 0.0
+    V = torch.eye(N, dtype=dtype)[:: N // 2]  # Only used in testing
+    V = repeat(V, "n m -> h n m", h=H)
+
+    return w, P, B, V
+
+
+def ssm(measure, N, R, H, **ssm_args):
+    """Dispatcher to create single SSM initialization
+
+    N: state size
+    R: rank (for DPLR parameterization)
+    H: number of independent SSM copies
+    """
+
+    if measure == "dplr":
+        w, P, B, V = dplr(N=N, rank=R, H=H, **ssm_args)
+    elif measure.startswith("diag"):
+        args = measure.split("-")
+        assert args[0] == "diag" and len(args) > 1
+        scaling = args[1]
+        w, P, B, V = dplr(
+            scaling=scaling, N=N, rank=R, H=H, diagonal=True, **ssm_args
+        )
+    else:
+        w, P, B, V = nplr(measure, N, R, **ssm_args)
+        w = repeat(w, "n -> s n", s=H)
+        P = repeat(P, "r n -> r s n", s=H)
+        B = repeat(B, "n -> s n", s=H)
+        V = repeat(V, "n m -> s n m", s=H)
+    return w, P, B, V
+
+
+combinations = {
+    "hippo": ["legs", "fourier"],
+    "diag": ["diag-inv", "diag-lin"],
+    "all": ["legs", "fourier", "diag-inv", "diag-lin"],
+}
+
+
+def combination(measures, N, R, S, **ssm_args):
+    if isinstance(measures, str):
+        measures = (
+            combinations[measures] if measures in combinations else [measures]
+        )
+
+    assert (
+        S % len(measures) == 0
+    ), f"{S} independent trainable SSM copies must be multiple of {len(measures)} different measures"
+    w, P, B, V = zip(
+        *[
+            ssm(measure, N, R, S // len(measures), **ssm_args)
+            for measure in measures
+        ]
+    )
+    w = torch.cat(w, dim=0)  # (S N)
+    P = torch.cat(P, dim=1)  # (R S N)
+    B = torch.cat(B, dim=0)  # (S N)
+    V = torch.cat(V, dim=0)  # (S N N)
+    return w, P, B, V
+
+
+class OptimModule(nn.Module):
+    """Interface for Module that allows registering buffers/parameters with configurable optimizer hyperparameters"""
+
+    def register(self, name, tensor, lr=None):
+        """Register a tensor with a configurable learning rate and 0 weight decay"""
+
+        if lr == 0.0:
+            self.register_buffer(name, tensor)
+        else:
+            self.register_parameter(name, nn.Parameter(tensor))
+
+            optim = {"weight_decay": 0.0}
+            if lr is not None:
+                optim["lr"] = lr
+            setattr(getattr(self, name), "_optim", optim)
+
+
+class SSKernelNPLR(OptimModule):
+    """Stores a representation of and computes the SSKernel function K_L(A^dt, B^dt, C) corresponding to a discretized state space, where A is Normal + Low Rank (NPLR)"""
+
+    @torch.no_grad()
+    def _setup_C(self, L):
+        """Construct C~ from C
+
+        Two modes are supported: go directly to length L if self.L is 1, or length is doubled
+        """
+
+        if self.L.item() == 0:
+            if self.verbose:
+                log.info(f"S4: Initializing kernel to length {L}")
+            double_length = False
+        elif L > self.L.item():  # 2*int(self.L) == L:
+            if self.verbose:
+                log.info(
+                    f"S4: Doubling length from L = {self.L.item()} to {2*self.L.item()}"
+                )
+            double_length = True
+            L = self.L.item()  # Convenience for the math below
+        else:
+            return
+
+        C = _r2c(self.C)
+        dA, _ = self._setup_state()
+        dA_L = power(L, dA)
+        # Multiply C by I - dA_L
+        C_ = _conj(C)
+        prod = contract("h m n, c h n -> c h m", dA_L.transpose(-1, -2), C_)
+        if double_length:
+            prod = -prod  # Multiply by I + dA_L instead
+        C_ = C_ - prod
+        C_ = C_[..., : self.N]  # Take conjugate pairs again
+        self.C.copy_(_c2r(C_))
+
+        self.L = (
+            2 * self.L if double_length else self.L + L
+        )  # Preserve type/device
+
+    def _omega(self, L, dtype, device, cache=True):
+        """Calculate (and cache) FFT nodes and their "unprocessed" version with the bilinear transform
+        This should be called everytime the internal length self.L changes"""
+
+        # Use cached if available
+        if (
+            cache
+            and hasattr(self, "omega")
+            and self.omega.size(-1) == L // 2 + 1
+        ):
+            return self.omega, self.z
+
+        omega = torch.tensor(
+            np.exp(-2j * np.pi / (L)), dtype=dtype, device=device
+        )  # \omega_{2L}
+        omega = omega ** torch.arange(0, L // 2 + 1, device=device)
+        z = 2 * (1 - omega) / (1 + omega)
+
+        # Cache if necessary
+        if cache:
+            self.omega = omega
+            self.z = z
+        return omega, z
+
+    def __init__(
+        self,
+        w,
+        P,
+        B,
+        C,
+        log_dt,
+        L=None,  # starting/maximum length of kernel
+        lr=None,
+        verbose=False,
+        keops=False,
+        real_type="exp",  # ['none' | 'exp' | 'relu' | sigmoid']
+        real_tolerance=1e-3,
+        bandlimit=None,
+    ):
+        """
+        L: Maximum length; this module computes an SSM kernel of length L
+        A is represented by diag(w) - PP^*
+        w: (S, N) diagonal part
+        P: (R, S, N) low-rank part
+
+        B: (S, N)
+        C: (C, H, N)
+        dt: (H) timescale per feature
+        lr: [dict | float | None] hook to set lr of special parameters (A, B, dt)
+
+        Dimensions:
+        N (or d_state): state size
+        H (or d_model): total SSM copies
+        S (or n_ssm): number of trainable copies of (A, B, dt); must divide H
+        R (or rank): rank of low-rank part
+        C (or channels): system is 1-dim to C-dim
+
+        The forward pass of this Module returns a tensor of shape (C, H, L)
+
+        Note: tensor shape N here denotes half the true state size, because of conjugate symmetry
+        """
+
+        super().__init__()
+        self.verbose = verbose
+        self.keops = keops
+        self.bandlimit = bandlimit
+        self.real_type = real_type
+        self.real_tolerance = real_tolerance
+
+        # Rank of low-rank correction
+        self.rank = P.shape[-3]
+        assert w.size(-1) == P.size(-1) == B.size(-1) == C.size(-1)
+        self.H = log_dt.size(-1)
+        self.N = w.size(-1)
+
+        # Check different SSM inits
+        assert w.size(-2) == P.size(-2) == B.size(-2)  # n_ssm
+        assert self.H % w.size(0) == 0
+        self.n_ssm = w.size(0)
+        self.repeat = self.H // w.size(
+            0
+        )  # Each trainable SSM needs to be duplicated this many times
+
+        # Broadcast everything to correct shapes
+        C = C.expand(
+            torch.broadcast_shapes(C.shape, (1, self.H, self.N))
+        )  # (C, H, N)
+        B = B.unsqueeze(0)  # (1, 1, N)
+
+        # Register parameters
+        self.C = nn.Parameter(_c2r(_resolve_conj(C)))
+        if lr is None or isinstance(lr, float):
+            lr_dict = {}
+        else:
+            lr_dict, lr = lr, None
+        self.register("log_dt", log_dt, lr_dict.get("dt", lr))
+        self.register("B", _c2r(B), lr_dict.get("B", lr))
+        self.register("P", _c2r(P), lr_dict.get("A", lr))
+        self.register("inv_w_real", self._w_init(w.real), lr_dict.get("A", lr))
+        self.register("w_imag", w.imag, lr_dict.get("A", lr))
+
+        self.l_max = L
+        self.register_buffer("L", torch.tensor(0))  # Internal length
+
+    def _w_init(self, w_real):
+        w_real = torch.clamp(w_real, max=-self.real_tolerance)
+        if self.real_type == "none":
+            return -w_real
+        elif self.real_type == "exp":
+            return torch.log(
+                -w_real
+            )  # Some of the HiPPO methods have real part 0
+        elif self.real_type == "relu":
+            return -w_real
+        elif self.real_type == "sigmoid":
+            return torch.logit(-w_real)
+        elif self.real_type == "softplus":
+            return torch.log(torch.exp(-w_real) - 1)
+        else:
+            raise NotImplementedError
+
+    def _w(self):
+        # Get the internal w (diagonal) parameter
+        if self.real_type == "none":
+            w_real = -self.inv_w_real
+        elif self.real_type == "exp":
+            w_real = -torch.exp(self.inv_w_real)
+        elif self.real_type == "relu":
+            w_real = -F.relu(self.inv_w_real)
+        elif self.real_type == "sigmoid":
+            w_real = -F.sigmoid(self.inv_w_real)
+        elif self.real_type == "softplus":
+            w_real = -F.softplus(self.inv_w_real)
+        else:
+            raise NotImplementedError
+        w = w_real + 1j * self.w_imag
+        return w
+
+    def forward(self, state=None, rate=1.0, L=None):
+        """
+        state: (B, H, N) initial state
+        rate: sampling rate factor
+        L: target length
+
+        returns:
+        (C, H, L) convolution kernel (generally C=1)
+        (B, H, L) output from initial state
+        """
+
+        # Initialize C~ if necessary (done in forward pass so it's on the correct device)
+        if self.L.item() == 0 and self.l_max is not None and self.l_max > 0:
+            self._setup_C(self.l_max)
+
+        # Handle sampling rate logic
+        # The idea is that this kernel's length (in continuous units) is self.L, while we are asked to provide a kernel of length L at (relative) frequency rate
+        if L is None:
+            L = round(self.L.item() / rate)
+
+        # Increase the internal length if needed
+        continuous_L = round(rate * L)
+        while continuous_L > self.L.item():
+            self._setup_C(continuous_L)
+        discrete_L = round(self.L.item() / rate)
+
+        dt = torch.exp(self.log_dt) * rate
+        B = _r2c(self.B)
+        C = _r2c(self.C)
+        P = _r2c(self.P)
+        Q = P.conj()
+        w = self._w()  # (n_ssm, N)
+
+        # Address bandlimiting
+        if self.bandlimit is not None:
+            freqs = w.imag.abs() / (2 * math.pi)  # (H, N)
+            freqs = dt[:, None] / rate * freqs  # (H, N)
+            mask = torch.where(freqs < self.bandlimit * 0.5, 1, 0)
+            C = C * mask
+
+        # Get FFT nodes of right length
+        omega, z = self._omega(
+            discrete_L, dtype=w.dtype, device=w.device, cache=(rate == 1.0)
+        )
+
+        # Broadcast parameters to same hidden features H
+        B = repeat(B, "1 t n -> 1 (v t) n", v=self.repeat)
+        P = repeat(P, "r t n -> r (v t) n", v=self.repeat)
+        Q = repeat(Q, "r t n -> r (v t) n", v=self.repeat)
+        w = repeat(w, "t n -> (v t) n", v=self.repeat)
+
+        # Augment B
+        if state is not None:
+            # Have to "unbilinear" the state to put it into the same "type" as B
+            # Compute 1/dt * (I + dt/2 A) @ state
+
+            # Can do this without expanding (maybe minor speedup using conj symmetry in theory), but it's easier to read this way
+            s = _conj(state) if state.size(-1) == self.N else state  # (B H N)
+            sA = s * _conj(w) - contract(  # (B H N)
+                "bhm, rhm, rhn -> bhn", s, _conj(Q), _conj(P)
+            )
+            s = s / dt.unsqueeze(-1) + sA / 2
+            s = s[..., : self.N]
+
+            B = torch.cat([s, B], dim=-3)  # (B+1, H, N)
+
+        # Incorporate dt into A
+        w = w * dt.unsqueeze(-1)  # (H N)
+
+        # Stack B and p, C and q for convenient batching
+        B = torch.cat([B, P], dim=-3)  # (B+1+R, H, N)
+        C = torch.cat([C, Q], dim=-3)  # (C+R, H, N)
+
+        # Incorporate B and C batch dimensions
+        v = B.unsqueeze(-3) * C.unsqueeze(-4)  # (B+1+R, C+R, H, N)
+
+        # Calculate resolvent at omega
+        if has_cauchy_extension and z.dtype == torch.cfloat and not self.keops:
+            r = cauchy_mult(v, z, w, symmetric=True)
+        elif has_pykeops:
+            r = cauchy_conj(v, z, w)
+        else:
+            r = cauchy_naive(v, z, w)
+        r = r * dt[None, None, :, None]  # (B+1+R, C+R, H, L)
+
+        # Low-rank Woodbury correction
+        if self.rank == 1:
+            k_f = r[:-1, :-1, :, :] - r[:-1, -1:, :, :] * r[-1:, :-1, :, :] / (
+                1 + r[-1:, -1:, :, :]
+            )
+        elif self.rank == 2:
+            r00 = r[: -self.rank, : -self.rank, :, :]
+            r01 = r[: -self.rank, -self.rank :, :, :]
+            r10 = r[-self.rank :, : -self.rank, :, :]
+            r11 = r[-self.rank :, -self.rank :, :, :]
+            det = (1 + r11[:1, :1, :, :]) * (1 + r11[1:, 1:, :, :]) - r11[
+                :1, 1:, :, :
+            ] * r11[1:, :1, :, :]
+            s = (
+                r01[:, :1, :, :] * (1 + r11[1:, 1:, :, :]) * r10[:1, :, :, :]
+                + r01[:, 1:, :, :] * (1 + r11[:1, :1, :, :]) * r10[1:, :, :, :]
+                - r01[:, :1, :, :] * (r11[:1, 1:, :, :]) * r10[1:, :, :, :]
+                - r01[:, 1:, :, :] * (r11[1:, :1, :, :]) * r10[:1, :, :, :]
+            )
+            s = s / det
+            k_f = r00 - s
+        else:
+            r00 = r[: -self.rank, : -self.rank, :, :]
+            r01 = r[: -self.rank, -self.rank :, :, :]
+            r10 = r[-self.rank :, : -self.rank, :, :]
+            r11 = r[-self.rank :, -self.rank :, :, :]
+            r11 = rearrange(r11, "a b h n -> h n a b")
+            r11 = torch.linalg.inv(torch.eye(self.rank, device=r.device) + r11)
+            r11 = rearrange(r11, "h n a b -> a b h n")
+            k_f = r00 - torch.einsum(
+                "i j h n, j k h n, k l h n -> i l h n", r01, r11, r10
+            )
+
+        # Final correction for the bilinear transform
+        k_f = k_f * 2 / (1 + omega)
+
+        # Move from frequency to coefficients
+        k = torch.fft.irfft(k_f, n=discrete_L)  # (B+1, C, H, L)
+
+        # # Truncate to target length
+        k = k[..., :L]
+
+        if state is not None:
+            k_state = k[:-1, :, :, :]  # (B, C, H, L)
+        else:
+            k_state = None
+        k_B = k[-1, :, :, :]  # (C H L)
+
+        return k_B, k_state
+
+    @torch.no_grad()
+    def _setup_linear(self):
+        """Create parameters that allow fast linear stepping of state"""
+        w = self._w()
+        B = _r2c(self.B)  # (H N)
+        P = _r2c(self.P)
+        Q = P.conj()
+
+        # Repeat w shape properly
+        B = repeat(B, "1 t n -> 1 (v t) n", v=self.repeat)
+        P = repeat(P, "r t n -> r (v t) n", v=self.repeat)
+        Q = repeat(Q, "r t n -> r (v t) n", v=self.repeat)
+        w = repeat(w, "t n -> (v t) n", v=self.repeat)
+
+        # Prepare Linear stepping
+        dt = torch.exp(self.log_dt)
+        D = (2.0 / dt.unsqueeze(-1) - w).reciprocal()  # (H, N)
+        R = (
+            torch.eye(self.rank, dtype=w.dtype, device=w.device)
+            + 2 * contract("r h n, h n, s h n -> h r s", Q, D, P).real
+        )  # (H R R)
+        Q_D = rearrange(Q * D, "r h n -> h r n")
+        try:
+            R = torch.linalg.solve(R, Q_D)  # (H R N)
+        except Exception:
+            R = torch.tensor(
+                np.linalg.solve(
+                    R.to(Q_D).contiguous().detach().cpu(),
+                    Q_D.contiguous().detach().cpu(),
+                )
+            ).to(Q_D)
+        R = rearrange(R, "h r n -> r h n")
+
+        self.step_params = {
+            "D": D,  # (H N)
+            "R": R,  # (R H N)
+            "P": P,  # (R H N)
+            "Q": Q,  # (R H N)
+            "B": B,  # (1 H N)
+            "E": 2.0 / dt.unsqueeze(-1) + w,  # (H N)
+        }
+
+    def _step_state_linear(self, u=None, state=None):
+        """
+        Version of the step function that has time O(N) instead of O(N^2) per step, which takes advantage of the DPLR form and bilinear discretization.
+
+        Unfortunately, as currently implemented it's about 2x slower because it calls several sequential operations. Perhaps a fused CUDA kernel implementation would be much faster
+
+        u: (H) input
+        state: (H, N/2) state with conjugate pairs
+          Optionally, the state can have last dimension N
+        Returns: same shape as state
+        """
+        C = _r2c(self.C)  # View used for dtype/device
+
+        if u is None:  # Special case used to find dA
+            u = torch.zeros(self.H, dtype=C.dtype, device=C.device)
+        if state is None:  # Special case used to find dB
+            state = torch.zeros(self.H, self.N, dtype=C.dtype, device=C.device)
+
+        step_params = self.step_params.copy()
+        if (
+            state.size(-1) == self.N
+        ):  # Only store half of the conjugate pairs; should be true by default
+            # There should be a slightly faster way using conjugate symmetry
+            def contract_fn(p, x, y):
+                return contract(
+                    "r h n, r h m, ... h m -> ... h n",
+                    _conj(p),
+                    _conj(x),
+                    _conj(y),
+                )[
+                    ..., : self.N
+                ]  # inner outer product
+
+        else:
+            assert state.size(-1) == 2 * self.N
+            step_params = {k: _conj(v) for k, v in step_params.items()}
+
+            # TODO worth setting up a contract_expression in default_state if we want to use this at inference time for stepping
+            def contract_fn(p, x, y):
+                return contract(
+                    "r h n, r h m, ... h m -> ... h n", p, x, y
+                )  # inner outer product
+
+        D = step_params["D"]  # (H N)
+        E = step_params["E"]  # (H N)
+        R = step_params["R"]  # (R H N)
+        P = step_params["P"]  # (R H N)
+        Q = step_params["Q"]  # (R H N)
+        B = step_params["B"]  # (1 H N)
+
+        new_state = E * state - contract_fn(P, Q, state)  # (B H N)
+        new_state = new_state + 2.0 * B * u.unsqueeze(-1)  # (B H N)
+        new_state = D * (new_state - contract_fn(P, R, new_state))
+
+        return new_state
+
+    def _setup_state(self):
+        """Construct dA and dB for discretized state equation"""
+
+        # Construct dA and dB by using the stepping
+        self._setup_linear()
+        C = _r2c(
+            self.C
+        )  # Just returns a view that we use for finding dtype/device
+
+        state = torch.eye(
+            2 * self.N, dtype=C.dtype, device=C.device
+        ).unsqueeze(
+            -2
+        )  # (N 1 N)
+        dA = self._step_state_linear(state=state)
+        dA = rearrange(dA, "n h m -> h m n")
+
+        u = C.new_ones(self.H)
+        dB = self._step_state_linear(u=u)
+        dB = _conj(dB)
+        dB = rearrange(dB, "1 h n -> h n")  # (H N)
+        return dA, dB
+
+    def _step_state(self, u, state):
+        """Must be called after self.default_state() is used to construct an initial state!"""
+        next_state = self.state_contraction(
+            self.dA, state
+        ) + self.input_contraction(self.dB, u)
+        return next_state
+
+    def _setup_step(self, mode="dense"):
+        """Set up dA, dB, dC discretized parameters for stepping"""
+        self.dA, self.dB = self._setup_state()
+
+        # Calculate original C
+        C = _conj(_r2c(self.C))  # (H C N)
+        if self.L.item() == 0:
+            dC = C
+        else:
+            # self.C represents C_tilde
+            dA_L = power(self.L.item(), self.dA)
+            I = torch.eye(self.dA.size(-1)).to(dA_L)
+
+            dC = torch.linalg.solve(
+                I - dA_L.transpose(-1, -2),
+                C.unsqueeze(-1),
+            ).squeeze(-1)
+        self.dC = dC
+
+        # Do special preprocessing for different step modes
+
+        self._step_mode = mode
+        if mode == "linear":
+            # Linear case: special step function for the state, we need to handle output
+            # use conjugate symmetry by default, which affects the output projection
+            self.dC = 2 * self.dC[:, :, : self.N]
+        elif mode == "diagonal":
+            # Eigendecomposition of the A matrix
+            L, V = torch.linalg.eig(self.dA)
+            V_inv = torch.linalg.inv(V)
+            # Check that the eigendedecomposition is correct
+            if self.verbose:
+                print(
+                    "Diagonalization error:",
+                    torch.dist(V @ torch.diag_embed(L) @ V_inv, self.dA),
+                )
+
+            # Change the parameterization to diagonalize
+            self.dA = L
+            self.dB = contract("h n m, h m -> h n", V_inv, self.dB)
+            self.dC = contract("h n m, c h n -> c h m", V, self.dC)
+
+        elif mode == "dense":
+            pass
+        else:
+            raise NotImplementedError(
+                "NPLR Kernel step mode must be {'dense' | 'linear' | 'diagonal'}"
+            )
+
+    def default_state(self, *batch_shape):
+        C = _r2c(self.C)
+        N = C.size(-1)
+        H = C.size(-2)
+
+        # Cache the tensor contractions we will later do, for efficiency
+        # These are put in this function because they depend on the batch size
+        step_mode = getattr(
+            self, "_step_mode", "dense"
+        )  # Used in default_state, which is called without _setup_step() in forward_state()
+        if step_mode != "linear":
+            N *= 2
+
+            if step_mode == "diagonal":
+                self.state_contraction = contract_expression(
+                    "h n, ... h n -> ... h n",
+                    (H, N),
+                    batch_shape + (H, N),
+                )
+            else:
+                # Dense (quadratic) case: expand all terms
+                self.state_contraction = contract_expression(
+                    "h m n, ... h n -> ... h m",
+                    (H, N, N),
+                    batch_shape + (H, N),
+                )
+
+            self.input_contraction = contract_expression(
+                "h n, ... h -> ... h n",
+                (H, N),  # self.dB.shape
+                batch_shape + (H,),
+            )
+
+        self.output_contraction = contract_expression(
+            "c h n, ... h n -> ... c h",
+            (C.shape[0], H, N),  # self.dC.shape
+            batch_shape + (H, N),
+        )
+
+        state = torch.zeros(*batch_shape, H, N, dtype=C.dtype, device=C.device)
+        return state
+
+    def step(self, u, state):
+        """Must have called self._setup_step() and created state with self.default_state() before calling this"""
+
+        if self._step_mode == "linear":
+            new_state = self._step_state_linear(u, state)
+        else:
+            new_state = self._step_state(u, state)
+        y = self.output_contraction(self.dC, new_state)
+        return y.real, new_state
+
+
+class SSKernelDiag(OptimModule):
+    """Version using (complex) diagonal state matrix (S4D)"""
+
+    def __init__(
+        self,
+        A,
+        B,
+        C,
+        log_dt,
+        L=None,
+        disc="bilinear",
+        real_type="exp",
+        lr=None,
+        bandlimit=None,
+    ):
+        super().__init__()
+        self.L = L
+        self.disc = disc
+        self.bandlimit = bandlimit
+        self.real_type = real_type
+
+        # Rank of low-rank correction
+        assert A.size(-1) == C.size(-1)
+        self.H = log_dt.size(-1)
+        self.N = A.size(-1)
+        assert A.size(-2) == B.size(-2)  # Number of independent SSMs trained
+        assert self.H % A.size(-2) == 0
+        self.n_ssm = A.size(-2)
+        self.repeat = self.H // A.size(0)
+
+        self.channels = C.shape[0]
+        self.C = nn.Parameter(_c2r(_resolve_conj(C)))
+
+        # Register parameters
+        if lr is None or isinstance(lr, float):
+            lr_dict = {}
+        else:
+            lr_dict, lr = lr, None
+
+        self.register("log_dt", log_dt, lr_dict.get("dt", lr))
+        self.register("B", _c2r(B), lr_dict.get("B", lr))
+        self.register("inv_A_real", self._A_init(A.real), lr_dict.get("A", lr))
+        self.register("A_imag", A.imag, lr_dict.get("A", lr))
+
+    def _A_init(self, A_real):
+        A_real = torch.clamp(A_real, max=-1e-4)
+        if self.real_type == "none":
+            return -A_real
+        elif self.real_type == "exp":
+            return torch.log(
+                -A_real
+            )  # Some of the HiPPO methods have real part 0
+        elif self.real_type == "relu":
+            return -A_real
+        elif self.real_type == "sigmoid":
+            return torch.logit(-A_real)
+        elif self.real_type == "softplus":
+            return torch.log(torch.exp(-A_real) - 1)
+        else:
+            raise NotImplementedError
+
+    def _A(self):
+        # Get the internal A (diagonal) parameter
+        if self.real_type == "none":
+            A_real = -self.inv_A_real
+        elif self.real_type == "exp":
+            A_real = -torch.exp(self.inv_A_real)
+        elif self.real_type == "relu":
+            # JAX version seems to NaN if you alloA 0's, although this code Aas fine Aithout it
+            A_real = -F.relu(self.inv_A_real) - 1e-4
+        elif self.real_type == "sigmoid":
+            A_real = -F.sigmoid(self.inv_A_real)
+        elif self.real_type == "softplus":
+            A_real = -F.softplus(self.inv_A_real)
+        else:
+            raise NotImplementedError
+        A = A_real + 1j * self.A_imag
+        return A
+
+    def forward(self, L, state=None, rate=1.0, u=None):
+        """
+        state: (B, H, N) initial state
+        rate: sampling rate factor
+        L: target length
+
+        returns:
+        (C, H, L) convolution kernel (generally C=1)
+        (B, H, L) output from initial state
+        """
+
+        dt = torch.exp(self.log_dt) * rate  # (H)
+        C = _r2c(self.C)  # (C H N)
+        A = self._A()  # (H N)
+
+        B = _r2c(self.B)
+        B = repeat(B, "t n -> 1 (v t) n", v=self.repeat)
+
+        if self.bandlimit is not None:
+            freqs = dt[:, None] / rate * A.imag.abs() / (2 * math.pi)  # (H, N)
+            mask = torch.where(freqs < self.bandlimit * 0.5, 1, 0)
+            C = C * mask
+
+        # Incorporate dt into A
+        A = repeat(A, "t n -> (v t) n", v=self.repeat)
+        dtA = A * dt.unsqueeze(-1)  # (H N)
+
+        # Augment B with state
+        if state is not None:
+            s = state / dt.unsqueeze(-1)
+            if self.disc == "bilinear":
+                s = s * (1.0 + dtA / 2)
+            elif self.disc == "zoh":
+                s = s * dtA * dtA.exp() / (dtA.exp() - 1.0)
+            B = torch.cat([s, B], dim=-3)  # (1+B H N)
+
+        C = (B[:, None, :, :] * C).view(-1, self.H, self.N)
+        if self.disc == "zoh":
+            # Power up
+            C = C * (torch.exp(dtA) - 1.0) / A
+            K = log_vandermonde(C, dtA, L)  # (H L)
+        elif self.disc == "bilinear":
+            C = (
+                C * (1.0 - dtA / 2).reciprocal() * dt.unsqueeze(-1)
+            )  # or * dtA / A
+            dA = (1.0 + dtA / 2) / (1.0 - dtA / 2)
+            K = log_vandermonde(C, dA.log(), L)
+        elif self.disc == "dss":
+            # Implementation from DSS meant for case when real eigenvalues can be positive
+            P = dtA.unsqueeze(-1) * torch.arange(L, device=C.device)  # [H N L]
+            A_gt_0 = A.real > 0  # [N]
+            if A_gt_0.any():
+                with torch.no_grad():
+                    P_max = dtA * (A_gt_0 * (L - 1))  # [H N]
+                P = P - P_max.unsqueeze(-1)  # [H N L]
+            S = P.exp()  # [H N L]
+
+            dtA_neg = dtA * (1 - 2 * A_gt_0)  # [H N]
+            num = dtA_neg.exp() - 1  # [H N]
+            den = (dtA_neg * L).exp() - 1  # [H N]
+
+            # Inline reciprocal function for DSS logic
+            x = den * A
+            x_conj = _resolve_conj(x)
+            r = x_conj / (x * x_conj + 1e-7)
+
+            C = C * num * r  # [C H N]
+            K = contract("chn,hnl->chl", C, S).float()
+        else:
+            assert False, f"{self.disc} not supported"
+
+        K = K.view(-1, self.channels, self.H, L)  # (1+B C H L)
+        if state is not None:
+            K_state = K[:-1, :, :, :]  # (B C H L)
+        else:
+            K_state = None
+        K = K[-1, :, :, :]  # (C H L)
+        return K, K_state
+
+    def _setup_step(self):
+        # These methods are organized like this to be compatible with the NPLR kernel interface
+        dt = torch.exp(self.log_dt)  # (H)
+        B = _r2c(self.B)  # (H N)
+        C = _r2c(self.C)  # (C H N)
+        self.dC = C
+        A = self._A()  # (H N)
+
+        A = repeat(A, "t n -> (v t) n", v=self.repeat)
+        B = repeat(B, "t n -> (v t) n", v=self.repeat)
+
+        # Incorporate dt into A
+        dtA = A * dt.unsqueeze(-1)  # (H N)
+        if self.disc == "zoh":
+            self.dA = torch.exp(dtA)  # (H N)
+            self.dB = B * (torch.exp(dtA) - 1.0) / A  # (C H N)
+        elif self.disc == "bilinear":
+            self.dA = (1.0 + dtA / 2) / (1.0 - dtA / 2)
+            self.dB = (
+                B * (1.0 - dtA / 2).reciprocal() * dt.unsqueeze(-1)
+            )  # or * dtA / A
+
+    def default_state(self, *batch_shape):
+        C = _r2c(self.C)
+        state = torch.zeros(
+            *batch_shape, self.H, self.N, dtype=C.dtype, device=C.device
+        )
+        return state
+
+    def step(self, u, state):
+        next_state = contract(
+            "h n, b h n -> b h n", self.dA, state
+        ) + contract("h n, b h -> b h n", self.dB, u)
+        y = contract("c h n, b h n -> b c h", self.dC, next_state)
+        return 2 * y.real, next_state
+
+    def forward_state(self, u, state):
+        self._setup_step()
+        AL = self.dA ** u.size(-1)
+        u = u.flip(-1).to(self.dA).contiguous()  # (B H L)
+        v = log_vandermonde_transpose(u, self.dB, self.dA.log(), u.size(-1))
+        next_state = AL * state + v
+        return next_state
+
+
+class SSKernel(nn.Module):
+    """Wrapper around SSKernel parameterizations.
+
+    The SSKernel is expected to support the interface
+    forward()
+    default_state()
+    _setup_step()
+    step()
+    """
+
+    def __init__(
+        self,
+        H,
+        N=64,
+        L=None,
+        measure="legs",
+        rank=1,
+        channels=1,
+        dt_min=0.001,
+        dt_max=0.1,
+        deterministic=False,
+        lr=None,
+        mode="nplr",
+        n_ssm=None,
+        verbose=False,
+        measure_args={},
+        **kernel_args,
+    ):
+        """State Space Kernel which computes the convolution kernel $\\bar{K}$
+
+        H: Number of independent SSM copies; controls the size of the model. Also called d_model in the config.
+        N: State size (dimensionality of parameters A, B, C). Also called d_state in the config. Generally shouldn't need to be adjusted and doens't affect speed much.
+        L: Maximum length of convolution kernel, if known. Should work in the majority of cases even if not known.
+        measure: Options for initialization of (A, B). For NPLR mode, recommendations are "legs", "fout", "hippo" (combination of both). For Diag mode, recommendations are "diag-inv", "diag-lin", "diag-legs", and "diag" (combination of diag-inv and diag-lin)
+        rank: Rank of low-rank correction for NPLR mode. Needs to be increased for measure "legt"
+        channels: C channels turns the SSM from a 1-dim to C-dim map; can think of it having C separate "heads" per SSM. This was partly a feature to make it easier to implement bidirectionality; it is recommended to set channels=1 and adjust H to control parameters instead
+        dt_min, dt_max: min and max values for the step size dt (\Delta)
+        mode: Which kernel algorithm to use. 'nplr' is the full S4 model; 'diag' is the simpler S4D; 'slow' is a dense version for testing
+        n_ssm: Number of independent trainable (A, B) SSMs, e.g. n_ssm=1 means all A/B parameters are tied across the H different instantiations of C. n_ssm=None means all H SSMs are completely independent. Generally, changing this option can save parameters but doesn't affect performance or speed much. This parameter must divide H
+        lr: Passing in a number (e.g. 0.001) sets attributes of SSM parameers (A, B, dt). A custom optimizer hook is needed to configure the optimizer to set the learning rates appropriately for these parameters.
+        """
+        super().__init__()
+        self.N = N
+        self.H = H
+        dtype, cdtype = torch.float, torch.cfloat
+        self.channels = channels
+        self.n_ssm = n_ssm if n_ssm is not None else H
+        self.mode = mode
+        self.verbose = verbose
+        self.kernel_args = kernel_args
+
+        # Generate dt
+        if deterministic:
+            log_dt = torch.exp(
+                torch.linspace(math.log(dt_min), math.log(dt_max), H)
+            )
+        else:
+            log_dt = torch.rand(self.H, dtype=dtype) * (
+                math.log(dt_max) - math.log(dt_min)
+            ) + math.log(dt_min)
+
+        # Compute the preprocessed representation
+        w, P, B, V = combination(
+            measure, self.N, rank, self.n_ssm, **measure_args
+        )
+
+        # Broadcast C to have H channels
+        if deterministic:
+            C = torch.zeros(channels, self.n_ssm, self.N, dtype=cdtype)
+            C[:, :, :1] = 1.0
+            C = contract(
+                "hmn, chn -> chm", V.conj().transpose(-1, -2), C
+            )  # V^* C
+            C = (
+                repeat(C, "c t n -> c (v t) n", v=self.n_ssm // C.size(-2))
+                .clone()
+                .contiguous()
+            )
+        else:
+            C = torch.randn(channels, self.H, self.N // 2, dtype=cdtype)
+
+        # Broadcast other parameters to have n_ssm copies
+        assert (
+            self.n_ssm % B.size(-2) == 0
+            and self.n_ssm % P.size(-2) == 0
+            and self.n_ssm % w.size(-2) == 0
+        )
+        # Broadcast tensors to n_ssm copies
+        # These will be the parameters, so make sure tensors are materialized and contiguous
+        B = (
+            repeat(B, "t n -> (v t) n", v=self.n_ssm // B.size(-2))
+            .clone()
+            .contiguous()
+        )
+        P = (
+            repeat(P, "r t n -> r (v t) n", v=self.n_ssm // P.size(-2))
+            .clone()
+            .contiguous()
+        )
+        w = (
+            repeat(w, "t n -> (v t) n", v=self.n_ssm // w.size(-2))
+            .clone()
+            .contiguous()
+        )
+
+        if mode == "nplr":
+            self.kernel = SSKernelNPLR(
+                w,
+                P,
+                B,
+                C,
+                log_dt,
+                L=L,
+                lr=lr,
+                verbose=verbose,
+                **kernel_args,
+            )
+        elif mode == "diag":
+            if not measure.startswith("diag"):
+                log.warning(
+                    "Diagonal kernel (S4D) activated but initialization is not intended for S4D. Set `measure` to 'diag-lin', 'diag-inv', or 'diag-legs' for the main variants, or 'diag' for a combination of S4D-Lin and S4D-Inv."
+                )
+            C = C * repeat(B, "t n -> (v t) n", v=H // self.n_ssm)
+            self.kernel = SSKernelDiag(
+                w,
+                B,
+                C,
+                log_dt,
+                L=L,
+                lr=lr,
+                **kernel_args,
+            )
+        else:
+            raise NotImplementedError(f"{mode=} is not valid")
+
+    def forward(self, state=None, L=None, rate=1.0):
+        return self.kernel(state=state, L=L, rate=rate)
+
+    @torch.no_grad()
+    def forward_state(self, u, state):
+        """Forward the state through a sequence, i.e. computes the state after passing chunk through SSM
+
+        state: (B, H, N)
+        u: (B, H, L)
+
+        Returns: (B, H, N)
+        """
+
+        if hasattr(self.kernel, "forward_state"):
+            return self.kernel.forward_state(u, state)
+
+        dA, dB = self.kernel._setup_state()  # Construct dA, dB matrices
+        # dA, dB = self.kernel.dA, self.kernel.dB # (H N N) (H N)
+
+        conj = state.size(-1) != dA.size(-1)
+        if conj:
+            state = _conj(state)
+
+        v = contract(
+            "h n, b h l -> b h n l", dB, u.flip(-1)
+        )  # dB.unsqueeze(-1) * u.flip(-1).unsqueeze(-2)
+        AL, v = power(u.size(-1), dA, v)
+        next_state = contract("h m n, b h n -> b h m", AL, state)
+        next_state = next_state + v
+
+        if conj:
+            next_state = next_state[..., : next_state.size(-1) // 2]
+        return next_state
+
+    def _setup_step(self, **kwargs):
+        # This method is intended to be private so that setting up an S4 module with
+        # ```
+        # if hasattr(module, 'setup_step'): module.setup_step()
+        # ```
+        # will not trigger this method multiple times
+        self.kernel._setup_step(**kwargs)
+
+    def step(self, u, state, **kwargs):
+        y, state = self.kernel.step(u, state, **kwargs)
+        return y, state
+
+    def default_state(self, *args, **kwargs):
+        return self.kernel.default_state(*args, **kwargs)
+
+
+class S4(nn.Module):
+    def __init__(
+        self,
+        d_model,
+        d_state=64,
+        l_max=None,
+        channels=1,
+        mode="nplr",
+        measure="legs",
+        bidirectional=False,
+        # Arguments for position-wise feedforward components
+        activation="gelu",
+        postact="glu",
+        hyper_act=None,
+        dropout=0.0,
+        tie_dropout=False,
+        bottleneck=None,
+        gate=None,
+        transposed=True,
+        verbose=False,
+        # SSM Kernel arguments
+        **kernel_args,
+    ):
+        """
+        d_state: the dimension of the state, also denoted by N
+        l_max: the maximum kernel length, also denoted by L. Set l_max=None to always use a global kernel
+        channels: can be interpreted as a number of "heads"; the SSM is a map from a 1-dim to C-dim sequence. It's not recommended to change this unless desperate for things to tune; instead, increase d_model for larger models
+        bidirectional: if True, convolution kernel will be two-sided
+
+        Position-wise feedforward components:
+        --------------------
+        activation: activation in between SS and FF
+        postact: activation after FF
+        hyper_act: use a "hypernetwork" multiplication (experimental)
+        dropout: standard dropout argument. tie_dropout=True ties the dropout mask across the sequence length, emulating nn.Dropout1d
+
+        Other arguments:
+        --------------------
+        transposed: choose backbone axis ordering of (B, L, H) (if False) or (B, H, L) (if True) [B=batch size, L=sequence length, H=hidden dimension]
+        gate: add gated activation (GSS)
+        bottleneck: reduce SSM dimension (GSS)
+
+        See the class SSKernel for the kernel constructor which accepts kernel_args. Relevant options that are worth considering and tuning include "mode" + "measure", "dt_min", "dt_max", "lr"
+
+        Other options are all experimental and should not need to be configured
+        """
+
+        super().__init__()
+        if verbose:
+            log.info(
+                f"Constructing S4 (H, N, L) = ({d_model}, {d_state}, {l_max})"
+            )
+
+        self.d_model = d_model
+        self.H = d_model
+        self.N = d_state
+        self.L = l_max
+        self.bidirectional = bidirectional
+        self.channels = channels
+        self.transposed = transposed
+
+        self.gate = gate
+        self.bottleneck = bottleneck
+
+        if bottleneck is not None:
+            self.H = self.H // bottleneck
+            self.input_linear = LinearActivation(
+                self.d_model,
+                self.H,
+                transposed=self.transposed,
+                activation=activation,
+                activate=True,
+            )
+
+        if gate is not None:
+            self.input_gate = LinearActivation(
+                self.d_model,
+                self.d_model * gate,
+                transposed=self.transposed,
+                activation=activation,
+                activate=True,
+            )
+            self.output_gate = LinearActivation(
+                self.d_model * gate,
+                self.d_model,
+                transposed=self.transposed,
+                activation=None,
+                activate=False,
+            )
+
+        # optional multiplicative modulation GLU-style
+        # https://arxiv.org/abs/2002.05202
+        self.hyper = hyper_act is not None
+        if self.hyper:
+            channels *= 2
+            self.hyper_activation = Activation(hyper_act)
+
+        self.D = nn.Parameter(torch.randn(channels, self.H))
+
+        if self.bidirectional:
+            channels *= 2
+
+        # SSM Kernel
+        self.kernel = SSKernel(
+            self.H,
+            N=self.N,
+            L=self.L,
+            channels=channels,
+            verbose=verbose,
+            mode=mode,
+            measure=measure,
+            **kernel_args,
+        )
+
+        # Pointwise
+        self.activation = Activation(activation)
+        dropout_fn = DropoutNd if tie_dropout else nn.Dropout
+        self.dropout = dropout_fn(dropout) if dropout > 0.0 else nn.Identity()
+        # position-wise output transform to mix features
+        self.output_linear = LinearActivation(
+            self.H * self.channels,
+            self.d_model * (1 if self.gate is None else self.gate),
+            transposed=self.transposed,
+            activation=postact,
+            activate=True,
+        )
+
+    def forward(self, u, state=None, rate=1.0, lengths=None, **kwargs):
+        """
+        u: (B H L) if self.transposed else (B L H)
+        state: (H N) never needed unless you know what you're doing
+
+        Returns: same shape as u
+        """
+        if not self.transposed:
+            u = u.transpose(-1, -2)
+        L = u.size(-1)
+
+        # Mask out padding tokens
+        if isinstance(lengths, int):
+            if lengths != L:
+                lengths = torch.tensor(
+                    lengths, dtype=torch.long, device=u.device
+                )
+            else:
+                lengths = None
+        if lengths is not None:
+            assert (
+                isinstance(lengths, torch.Tensor)
+                and lengths.ndim == 1
+                and lengths.size(0) in [1, u.size(0)]
+            )
+            mask = torch.where(
+                torch.arange(L, device=lengths.device)
+                < lengths[:, None, None],
+                1.0,
+                0.0,
+            )
+            u = u * mask
+
+        if self.gate is not None:
+            v = self.input_gate(u)
+        if self.bottleneck is not None:
+            u = self.input_linear(u)
+
+        # Compute SS Kernel
+        L_kernel = L if self.L is None else min(L, round(self.L / rate))
+        k, k_state = self.kernel(
+            L=L_kernel, rate=rate, state=state
+        )  # (C H L) (B C H L)
+
+        # Convolution
+        if self.bidirectional:
+            k0, k1 = rearrange(k, "(s c) h l -> s c h l", s=2)
+            k = F.pad(k0, (0, L)) + F.pad(k1.flip(-1), (L, 0))
+        k_f = torch.fft.rfft(k, n=L_kernel + L)  # (C H L)
+        u_f = torch.fft.rfft(u, n=L_kernel + L)  # (B H L)
+        y_f = contract("bhl,chl->bchl", u_f, k_f)
+        y = torch.fft.irfft(y_f, n=L_kernel + L)[..., :L]  # (B C H L)
+
+        # Compute D term in state space equation - essentially a skip connection
+        y = y + contract("bhl,ch->bchl", u, self.D)
+
+        # Compute state update
+        if state is not None:
+            assert (
+                not self.bidirectional
+            ), "Bidirectional not supported with state forwarding"
+            y = y + k_state  #
+            next_state = self.kernel.forward_state(u, state)
+        else:
+            next_state = None
+
+        # Optional hyper-network multiplication
+        if self.hyper:
+            y, yh = rearrange(y, "b (s c) h l -> s b c h l", s=2)
+            y = self.hyper_activation(yh) * y
+
+        # Reshape to flatten channels
+        y = rearrange(y, "... c h l -> ... (c h) l")
+
+        y = self.dropout(self.activation(y))
+
+        if not self.transposed:
+            y = y.transpose(-1, -2)
+
+        y = self.output_linear(y)
+
+        if self.gate is not None:
+            y = self.output_gate(y * v)
+
+        return y, next_state
+
+    def setup_step(self, **kwargs):
+        self.kernel._setup_step(**kwargs)
+
+    def step(self, u, state):
+        """Step one time step as a recurrent model. Intended to be used during validation.
+
+        u: (B H)
+        state: (B H N)
+        Returns: output (B H), state (B H N)
+        """
+        assert not self.training
+
+        y, next_state = self.kernel.step(u, state)  # (B C H)
+        y = y + u.unsqueeze(-2) * self.D
+        y = rearrange(y, "b c h -> b (c h)")
+        y = self.activation(y)
+        if self.transposed:
+            y = self.output_linear(y.unsqueeze(-1)).squeeze(-1)
+        else:
+            y = self.output_linear(y)
+        return y, next_state
+
+    def default_state(self, *batch_shape, device=None):
+        # kernel is not a SequenceModule so it doesn't need to adhere to same interface
+        # the kernel will know the device of its own parameters
+        return self.kernel.default_state(*batch_shape)
+
+    @property
+    def d_output(self):
+        return self.d_model
diff --git a/probts/model/nn/layers/s4_backbones.py b/probts/model/nn/layers/s4_backbones.py
new file mode 100644
index 0000000..20bed5c
--- /dev/null
+++ b/probts/model/nn/layers/s4_backbones.py
@@ -0,0 +1,184 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+import math
+
+import torch
+from torch import nn
+
+from .s4 import S4
+
+
+class SinusoidalPositionEmbeddings(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, time):
+        device = time.device
+        half_dim = self.dim // 2
+        embeddings = math.log(10000) / (half_dim - 1)
+        embeddings = torch.exp(
+            torch.arange(half_dim, device=device) * -embeddings
+        )
+        embeddings = time[:, None] * embeddings[None, :]
+        embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)
+        return embeddings
+
+
+class S4Layer(nn.Module):
+    def __init__(
+        self,
+        d_model,
+        dropout=0.0,
+        mode="nplr",
+        l_max=None,
+        measure="legs"
+    ):
+        super().__init__()
+        self.layer = S4(
+            d_model=d_model,
+            d_state=128,
+            bidirectional=True,
+            dropout=dropout,
+            transposed=True,
+            postact=None,
+            mode=mode,
+            l_max=l_max,
+            measure=measure,
+        )
+        self.norm = nn.LayerNorm(d_model)
+        self.dropout = (
+            nn.Dropout1d(dropout) if dropout > 0.0 else nn.Identity()
+        )
+
+    def forward(self, x):
+        """
+        Input x is shape (B, d_input, L)
+        """
+        z = x
+        # Prenorm
+        z = self.norm(z.transpose(-1, -2)).transpose(-1, -2)
+        # Apply layer: we ignore the state input and output for training
+        z, _ = self.layer(z)
+        # Dropout on the output of the layer
+        z = self.dropout(z)
+        # Residual connection
+        x = z + x
+        return x, None
+
+    def default_state(self, *args, **kwargs):
+        return self.layer.default_state(*args, **kwargs)
+
+    def step(self, x, state, **kwargs):
+        z = x
+        # Prenorm
+        z = self.norm(z.transpose(-1, -2)).transpose(-1, -2)
+        # Apply layer
+        z, state = self.layer.step(z, state, **kwargs)
+        # Residual connection
+        x = z + x
+        return x, state
+
+
+class S4Block(nn.Module):
+    def __init__(self, d_model, dropout=0.0, expand=2, num_features=0,mode="nplr",l_max=None,measure="legs"):
+        super().__init__()
+        self.s4block = S4Layer(d_model, dropout=dropout,mode=mode,l_max=l_max,measure=measure)
+
+        self.time_linear = nn.Linear(d_model, d_model)
+        self.tanh = nn.Tanh()
+        self.sigm = nn.Sigmoid()
+        self.out_linear1 = nn.Conv1d(
+            in_channels=d_model, out_channels=d_model, kernel_size=1
+        )
+        self.out_linear2 = nn.Conv1d(
+            in_channels=d_model, out_channels=d_model, kernel_size=1
+        )
+        self.feature_encoder = nn.Conv1d(num_features, d_model, kernel_size=1)
+
+    def forward(self, x, t, features=None):
+        t = self.time_linear(t)[:, None, :].repeat(1, x.shape[2], 1)
+        t = t.transpose(-1, -2)
+        out, _ = self.s4block(x + t)
+        if features is not None:
+            out = out + self.feature_encoder(features)
+        out = self.tanh(out) * self.sigm(out)
+        out1 = self.out_linear1(out)
+        out2 = self.out_linear2(out)
+        return out1 + x, out2
+
+
+def Conv1dKaiming(in_channels, out_channels, kernel_size):
+    layer = nn.Conv1d(in_channels, out_channels, kernel_size)
+    nn.init.kaiming_normal_(layer.weight)
+    return layer
+
+
+class BackboneModel(nn.Module):
+    def __init__(
+        self,
+        input_dim,
+        hidden_dim,
+        output_dim,
+        step_emb,
+        num_residual_blocks,
+        num_features,
+        residual_block="s4",
+        mode="nplr",
+        measure="legs",
+        l_max=None,
+        dropout=0.0,
+        init_skip=True,
+    ):
+        super().__init__()
+        if residual_block == "s4":
+            residual_block = S4Block
+        else:
+            raise ValueError(f"Unknown residual block {residual_block}")
+        self.input_init = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.ReLU(),
+        )
+        self.time_init = nn.Sequential(
+            nn.Linear(step_emb, hidden_dim),
+            nn.SiLU(),
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.SiLU(),
+        )
+        self.out_linear = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, output_dim),
+        )
+        residual_blocks = []
+        for i in range(num_residual_blocks):
+            residual_blocks.append(
+                residual_block(
+                    hidden_dim, 
+                    num_features=num_features, 
+                    dropout=dropout, 
+                    mode=mode,l_max=l_max,
+                    measure=measure,
+                )
+            )
+        self.residual_blocks = nn.ModuleList(residual_blocks)
+        self.step_embedding = SinusoidalPositionEmbeddings(step_emb)
+        self.init_skip = init_skip
+
+    def forward(self, input, t, features=None):
+        x = self.input_init(input)  # B, L ,C
+        t = self.time_init(self.step_embedding(t))
+        x = x.transpose(-1, -2)
+        if features is not None:
+            features = features.transpose(-1, -2)
+        skips = []
+        for layer in self.residual_blocks:
+            x, skip = layer(x, t, features)
+            skips.append(skip)
+
+        skip = torch.stack(skips).sum(0)
+        skip = skip.transpose(-1, -2)
+        out = self.out_linear(skip)
+        if self.init_skip:
+            out = out + input
+        return out
diff --git a/probts/utils/__init__.py b/probts/utils/__init__.py
new file mode 100644
index 0000000..6ec98a7
--- /dev/null
+++ b/probts/utils/__init__.py
@@ -0,0 +1,2 @@
+from .utils import * 
+from .evaluator import Evaluator
\ No newline at end of file
diff --git a/probts/utils/download_datasets.py b/probts/utils/download_datasets.py
new file mode 100644
index 0000000..5523a25
--- /dev/null
+++ b/probts/utils/download_datasets.py
@@ -0,0 +1,12 @@
+from pathlib import Path
+from gluonts.dataset.repository.datasets import get_dataset, dataset_names
+from tqdm import tqdm
+
+
+if __name__ == '__main__':
+    save_path = Path('./datasets')
+    for dataset in tqdm(dataset_names):
+        print(f"generate {dataset}")
+        ds = get_dataset(dataset, path=save_path, regenerate=False)
+        print(ds.metadata)
+        print(len(ds.train))
\ No newline at end of file
diff --git a/probts/utils/evaluator.py b/probts/utils/evaluator.py
new file mode 100644
index 0000000..b5dc59d
--- /dev/null
+++ b/probts/utils/evaluator.py
@@ -0,0 +1,90 @@
+import numpy as np
+from .metrics import *
+
+class Evaluator:
+    
+    def __init__(self, quantiles_num=10, smooth=False):
+        self.quantiles = (1.0 * np.arange(quantiles_num) / quantiles_num)[1:]
+        self.ignore_invalid_values = True
+        self.smooth = smooth
+
+    def loss_name(self, q):
+        return f"QuantileLoss[{q}]"
+
+    def weighted_loss_name(self, q):
+        return f"wQuantileLoss[{q}]"
+
+    def coverage_name(self, q):
+        return f"Coverage[{q}]"
+
+    def get_metrics(self, targets, forecasts, samples_dim=0):
+        mean_forecasts = forecasts.mean(axis=samples_dim)
+        median_forecasts = np.quantile(forecasts, 0.5, axis=samples_dim)
+        metrics = {
+            "MSE": mse(targets, mean_forecasts),
+            "abs_error": abs_error(targets, median_forecasts),
+            "abs_target_sum": abs_target_sum(targets),
+            "abs_target_mean": abs_target_mean(targets),
+            "MAPE": mape(targets, median_forecasts),
+            "sMAPE": smape(targets, median_forecasts),
+        }
+        
+        metrics["RMSE"] = np.sqrt(metrics["MSE"])
+        metrics["NRMSE"] = metrics["RMSE"] / metrics["abs_target_mean"]
+        metrics["ND"] = metrics["abs_error"] / metrics["abs_target_sum"]
+        
+        for q in self.quantiles:
+            q_forecasts = np.quantile(forecasts, q, axis=samples_dim)
+            metrics[self.loss_name(q)] = quantile_loss(targets, q_forecasts, q)
+            metrics[self.weighted_loss_name(q)] = \
+                metrics[self.loss_name(q)] / metrics["abs_target_sum"]
+            metrics[self.coverage_name(q)] = coverage(targets, q_forecasts)
+        
+        metrics["mean_absolute_QuantileLoss"] = np.mean(
+            [metrics[self.loss_name(q)] for q in self.quantiles]
+        )
+        metrics["CRPS"] = np.mean(
+            [metrics[self.weighted_loss_name(q)] for q in self.quantiles]
+        )
+        metrics["MAE_Coverage"] = np.mean(
+            [
+                np.abs(metrics[self.coverage_name(q)] - np.array([q]))
+                for q in self.quantiles
+            ]
+        )
+        
+        return metrics
+
+    @property
+    def selected_metrics(self):
+        return ["CRPS", "ND", "NRMSE", "MSE"]
+
+    def __call__(self, targets, forecasts):
+        """
+
+        Parameters
+        ----------
+        targets
+            groundtruth in (batch_size, prediction_length, target_dim)
+        forecasts
+            forecasts in (batch_size, num_samples, prediction_length, target_dim)
+        Returns
+        -------
+        Dict[String, float]
+            metrics
+        """
+        targets = targets.cpu().detach().numpy()
+        forecasts = forecasts.cpu().detach().numpy()
+        if self.ignore_invalid_values:
+            targets = np.ma.masked_invalid(targets)
+            forecasts = np.ma.masked_invalid(forecasts)
+        
+        metrics = self.get_metrics(targets, forecasts, samples_dim=1)
+        metrics_sum = self.get_metrics(targets.sum(axis=-1), forecasts.sum(axis=-1), samples_dim=1)
+        
+        # select output metrics
+        output_metrics = dict()
+        for k in self.selected_metrics:
+            output_metrics[k] = metrics[k]
+            output_metrics[f"{k}-Sum"] = metrics_sum[k]
+        return output_metrics
\ No newline at end of file
diff --git a/probts/utils/masking.py b/probts/utils/masking.py
new file mode 100644
index 0000000..8158fdb
--- /dev/null
+++ b/probts/utils/masking.py
@@ -0,0 +1,26 @@
+# Code implementation from https://github.com/thuml/iTransformer
+import torch
+
+class TriangularCausalMask():
+    def __init__(self, B, L, device="cpu"):
+        mask_shape = [B, 1, L, L]
+        with torch.no_grad():
+            self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)
+
+    @property
+    def mask(self):
+        return self._mask
+
+
+class ProbMask():
+    def __init__(self, B, H, L, index, scores, device="cpu"):
+        _mask = torch.ones(L, scores.shape[-1], dtype=torch.bool).to(device).triu(1)
+        _mask_ex = _mask[None, None, :].expand(B, H, L, scores.shape[-1])
+        indicator = _mask_ex[torch.arange(B)[:, None, None],
+                    torch.arange(H)[None, :, None],
+                    index, :].to(device)
+        self._mask = indicator.view(scores.shape).to(device)
+
+    @property
+    def mask(self):
+        return self._mask
diff --git a/probts/utils/metrics.py b/probts/utils/metrics.py
new file mode 100644
index 0000000..6bb3b4b
--- /dev/null
+++ b/probts/utils/metrics.py
@@ -0,0 +1,80 @@
+
+import numpy as np
+
+
+def mse(target: np.ndarray, forecast: np.ndarray) -> float:
+    r"""
+    .. math::
+
+        mse = mean((Y - \hat{Y})^2)
+    """
+    return np.mean(np.square(target - forecast))
+
+
+def abs_error(target: np.ndarray, forecast: np.ndarray) -> float:
+    r"""
+    .. math::
+
+        abs\_error = sum(|Y - \hat{Y}|)
+    """
+    return np.sum(np.abs(target - forecast))
+
+
+def abs_target_sum(target) -> float:
+    r"""
+    .. math::
+
+        abs\_target\_sum = sum(|Y|)
+    """
+    return np.sum(np.abs(target))
+
+
+def abs_target_mean(target) -> float:
+    r"""
+    .. math::
+
+        abs\_target\_mean = mean(|Y|)
+    """
+    return np.mean(np.abs(target))
+
+
+def mape(target: np.ndarray, forecast: np.ndarray) -> float:
+    r"""
+    .. math::
+
+        mape = mean(|Y - \hat{Y}| / |Y|))
+
+    See [HA21]_ for more details.
+    """
+    return np.mean(np.abs(target - forecast) / np.abs(target))
+
+
+def smape(target: np.ndarray, forecast: np.ndarray) -> float:
+    r"""
+    .. math::
+
+        smape = 2 * mean(|Y - \hat{Y}| / (|Y| + |\hat{Y}|))
+
+    See [HA21]_ for more details.
+    """
+    return 2 * np.mean(
+        np.abs(target - forecast) / (np.abs(target) + np.abs(forecast))
+    )
+
+
+def quantile_loss(target: np.ndarray, forecast: np.ndarray, q: float) -> float:
+    r"""
+    .. math::
+
+        quantile\_loss = 2 * sum(|(Y - \hat{Y}) * ((Y <= \hat{Y}) - q)|)
+    """
+    return 2 * np.sum(np.abs((forecast - target) * ((target <= forecast) - q)))
+
+
+def coverage(target: np.ndarray, forecast: np.ndarray) -> float:
+    r"""
+    .. math::
+
+        coverage = mean(Y < \hat{Y})
+    """
+    return np.mean(target < forecast)
\ No newline at end of file
diff --git a/probts/utils/utils.py b/probts/utils/utils.py
new file mode 100644
index 0000000..927388a
--- /dev/null
+++ b/probts/utils/utils.py
@@ -0,0 +1,228 @@
+# ---------------------------------------------------------------------------------
+# Portions of this file are derived from PyTorch-TS
+# - Source: https://github.com/zalandoresearch/pytorch-ts
+# - License: MIT, Apache-2.0 license
+
+# We thank the authors for their contributions.
+# ---------------------------------------------------------------------------------
+
+
+import torch
+import numpy as np
+from typing import Optional, Dict
+
+
+class Scaler:
+    def __init__(self):
+        super().__init__()
+
+    def fit(self, values):
+        raise NotImplementedError
+
+    def transform(self, values):
+        raise NotImplementedError
+
+    def fit_transform(self, values):
+        raise NotImplementedError
+
+    def inverse_transform(self, values):
+        raise NotImplementedError
+
+
+class StandardScaler(Scaler):
+    def __init__(
+        self,
+        mean: float = None,
+        std: float = None,
+        epsilon: float = 1e-9,
+        var_specific: bool = True
+    ):
+        """
+        The class can be used to normalize PyTorch Tensors using native functions. The module does not expect the
+        tensors to be of any specific shape; as long as the features are the last dimension in the tensor, the module
+        will work fine.
+        
+        Args:
+            mean: The mean of the features. The property will be set after a call to fit.
+            std: The standard deviation of the features. The property will be set after a call to fit.
+            epsilon: Used to avoid a Division-By-Zero exception.
+            var_specific: If True, the mean and standard deviation will be computed per variate.
+        """
+        self.mean = mean
+        self.scale = std
+        self.epsilon = epsilon
+        self.var_specific = var_specific
+
+    def fit(self, values):
+        """
+        Args:
+            values: Input values should be a PyTorch tensor of shape (T, C) or (N, T, C), 
+                where N is the batch size, T is the timesteps and C is the number of variates.
+        """
+        dims = list(range(values.dim() - 1))
+        if not self.var_specific:
+            self.mean = torch.mean(values)
+            self.scale = torch.std(values)
+        else:
+            self.mean = torch.mean(values, dim=dims)
+            self.scale = torch.std(values, dim=dims)
+
+    def transform(self, values):
+        if self.mean is None:
+            return values
+
+        values = (values - self.mean.to(values.device)) / (self.scale.to(values.device) + self.epsilon)
+        return values.to(torch.float32)
+
+    def fit_transform(self, values):
+        self.fit(values)
+        return self.transform(values)
+
+    def inverse_transform(self, values):
+        if self.mean is None:
+            return values
+        
+        values = values * (self.scale.to(values.device) + self.epsilon)
+        values = values + self.mean.to(values.device)
+        return values
+
+
+class TemporalScaler(Scaler):
+    def __init__(
+        self,
+        minimum_scale:float = 1e-10,
+        time_first: bool = True
+    ):
+        """
+        The ``TemporalScaler`` computes a per-item scale according to the average
+        absolute value over time of each item. The average is computed only among
+        the observed values in the data tensor, as indicated by the second
+        argument. Items with no observed data are assigned a scale based on the
+        global average.
+
+        Args:
+            minimum_scale: default scale that is used if the time series has only zeros.
+            time_first: if True, the input tensor has shape (N, T, C), otherwise (N, C, T).
+        """
+        super().__init__()
+        self.scale = None
+        self.minimum_scale = torch.tensor(minimum_scale)
+        self.time_first = time_first
+
+    def fit(
+        self,
+        data: torch.Tensor,
+        observed_indicator: torch.Tensor = None
+    ):
+        """
+        Fit the scaler to the data.
+        
+        Args:
+            data: tensor of shape (N, T, C) if ``time_first == True`` or (N, C, T)
+                if ``time_first == False`` containing the data to be scaled
+
+            observed_indicator: observed_indicator: binary tensor with the same shape as
+                ``data``, that has 1 in correspondence of observed data points,
+                and 0 in correspondence of missing data points.
+
+        Note:
+            Tensor containing the scale, of shape (N, 1, C) or (N, C, 1).
+        """
+        if self.time_first:
+            dim = -2
+        else:
+            dim = -1
+
+        if observed_indicator is None:
+            observed_indicator = torch.ones_like(data)
+
+        # These will have shape (N, C)
+        num_observed = observed_indicator.sum(dim=dim)
+        sum_observed = (data.abs() * observed_indicator).sum(dim=dim)
+
+        # First compute a global scale per-dimension
+        total_observed = num_observed.sum(dim=0)
+        denominator = torch.max(total_observed, torch.ones_like(total_observed))
+        default_scale = sum_observed.sum(dim=0) / denominator
+
+        # Then compute a per-item, per-dimension scale
+        denominator = torch.max(num_observed, torch.ones_like(num_observed))
+        scale = sum_observed / denominator
+
+        # Use per-batch scale when no element is observed
+        # or when the sequence contains only zeros
+        scale = torch.where(
+            sum_observed > torch.zeros_like(sum_observed),
+            scale,
+            default_scale * torch.ones_like(num_observed),
+        )
+
+        self.scale = torch.max(scale, self.minimum_scale).unsqueeze(dim=dim).detach()
+
+    def transform(self, data):
+        return data / self.scale.to(data.device)
+
+    def fit_transform(self, data, observed_indicator=None):
+        self.fit(data, observed_indicator)
+        return self.transform(data)
+
+    def inverse_transform(self, data):
+        return data * self.scale.to(data.device)
+
+
+class IdentityScaler(Scaler):
+    """
+    No scaling is applied upon calling the ``IdentityScaler``.
+    """
+    def __init__(self, time_first: bool = True):
+        super().__init__()
+        self.scale = None
+        
+    def fit(self, data):
+        pass
+
+    def transform(self, data):
+        return data
+    
+    def inverse_transform(self, data):
+        return data
+
+
+def repeat(tensor: torch.Tensor, n: int, dim: int = 0):
+    return tensor.repeat_interleave(repeats=n, dim=dim)
+
+
+def extract(a, t, x_shape):
+    batch_size = t.shape[0]
+    out = a.gather(-1, t.cpu())
+    return out.reshape(batch_size, *((1,) * (len(x_shape) - 1))).to(t.device)
+
+
+def weighted_average(
+    x: torch.Tensor,
+    weights: Optional[torch.Tensor] = None,
+    dim: int = None
+):
+    """
+    Computes the weighted average of a given tensor across a given dim, masking
+    values associated with weight zero,
+    meaning instead of `nan * 0 = nan` you will get `0 * 0 = 0`.
+
+    Args:
+        x: Input tensor, of which the average must be computed.
+        weights: Weights tensor, of the same shape as `x`.
+        dim: The dim along which to average `x`
+
+    Returns:
+        Tensor: The tensor with values averaged along the specified `dim`.
+    """
+    if weights is not None:
+        weighted_tensor = torch.where(weights != 0, x * weights, torch.zeros_like(x))
+        sum_weights = torch.clamp(
+            weights.sum(dim=dim) if dim else weights.sum(), min=1.0
+        )
+        return (
+            weighted_tensor.sum(dim=dim) if dim else weighted_tensor.sum()
+        ) / sum_weights
+    else:
+        return x.mean(dim=dim)
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..8253fd8
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,68 @@
+[build-system]
+requires = ["setuptools>=66"]
+
+[project]
+name = "ProbTS"
+version = "0.1.0"
+description = "Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons"
+authors = [
+    {name = "Jiawen Zhang"},
+    {name = "Xumeng Wen"},
+    {name = "Zhenwei Zhang"},
+    {name = "Shun Zhen"},
+]
+readme = "README.md"
+requires-python = ">=3.10"
+license = {text = "MIT"}
+
+dependencies = [
+    "numpy",
+    "pandas",
+    "einops",
+    "matplotlib",
+    "tqdm",
+    "lightning @ https://github.com/Lightning-AI/lightning/archive/refs/heads/master.zip",
+    "gluonts==0.13.1",
+    "typeshed-client==2.3.0",
+    "docstring-parser==0.15",
+    "orjson==3.9.0",
+    "einops>=0.6.1",
+    "pydantic==1.10.8",
+    "linear-attention-transformer==0.19.1",
+    "ray==2.8.1",
+    "tensorboardx==2.6.2",
+    "pyarrow==11.0.0",
+    "protobuf>=3.19",
+    "jsonargparse[signatures]==4.27.7",
+    "opt_einsum",
+    "psutil",
+    "reformer-pytorch"
+]
+
+[project.optional-dependencies]
+tsfm = [
+    "timm",
+    "accelerate",
+    "tokenizers",
+    "datasets",
+    "transformers",
+    "chronos @ git+https://github.com/amazon-science/chronos-forecasting.git", # for chronos
+    "jaxtyping",
+    "hydra-core==1.3",
+    "orjson",
+    "tensorboard",
+    "multiprocess",
+    "huggingface_hub>=0.23.0",
+    "safetensors",
+    "jax[cpu]",
+    "paxml>=1.4.0", # for timesfm
+    "praxis>=1.4.0",
+    "einshape>=1.0.0",
+    "numpy>=1.26.4",
+    "pandas==2.0.3",
+    "utilsforecast",
+    "pykeops",
+]
+
+[tool.setuptools]
+py-modules = []
\ No newline at end of file
diff --git a/run.py b/run.py
new file mode 100644
index 0000000..2668f83
--- /dev/null
+++ b/run.py
@@ -0,0 +1,170 @@
+import os
+import json
+import torch
+import logging
+from probts.data import ProbTSDataModule
+from probts.model.forecast_module import ProbTSForecastModule
+from probts.callbacks import MemoryCallback, TimeCallback
+from lightning.pytorch.cli import LightningCLI
+from lightning.pytorch.loggers import CSVLogger, TensorBoardLogger
+from lightning.pytorch.callbacks import ModelCheckpoint
+
+import warnings
+warnings.filterwarnings('ignore')
+
+torch.set_float32_matmul_precision('high')
+
+log = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+
+
+class ProbTSCli(LightningCLI):
+    
+    def add_arguments_to_parser(self, parser):
+        data_to_model_link_args = [
+            "scaler"
+        ]
+        data_to_forecaster_link_args = [
+            "target_dim",
+            "history_length",
+            "context_length",
+            "prediction_length",
+            "lags_list",
+            "freq",
+            "time_feat_dim",
+            "global_mean"
+        ]
+        for arg in data_to_model_link_args:
+            parser.link_arguments(f"data.data_manager.{arg}", f"model.{arg}", apply_on="instantiate")
+        for arg in data_to_forecaster_link_args:
+            parser.link_arguments(f"data.data_manager.{arg}", f"model.forecaster.init_args.{arg}", apply_on="instantiate")
+
+    def init_exp(self):
+        config_args = self.parser.parse_args()
+        self.tag = "_".join([
+            self.datamodule.data_manager.dataset,
+            self.model.forecaster.name,
+            str(config_args.seed_everything)
+        ])
+        log.info(f"Root dir is {self.trainer.default_root_dir}, exp tag is {self.tag}")
+        
+        if not os.path.exists(self.trainer.default_root_dir):
+            os.makedirs(self.trainer.default_root_dir)
+
+        if self.model.load_from_ckpt is not None:
+            log.info(f"Loading pre-trained checkpoint from {self.model.load_from_ckpt}")
+            self.model = ProbTSForecastModule.load_from_checkpoint(
+                self.model.load_from_ckpt,
+                learning_rate=config_args.model.learning_rate,
+                scaler=self.datamodule.data_manager.scaler,
+                context_length=self.datamodule.data_manager.context_length,
+                target_dim=self.datamodule.data_manager.target_dim,
+                freq=self.datamodule.data_manager.freq,
+                prediction_length=self.datamodule.data_manager.prediction_length,
+                lags_list=self.datamodule.data_manager.lags_list,
+                time_feat_dim=self.datamodule.data_manager.time_feat_dim,
+            )
+        
+        # Set callbacks
+        self.checkpoint_callback = ModelCheckpoint(
+            dirpath=f'{self.trainer.default_root_dir}/ckpt/{self.tag}',
+            filename='{epoch}-{val_CRPS:.3f}',
+            every_n_epochs=1,
+            monitor='val_CRPS',
+            save_top_k=-1,
+            save_last=True,
+            enable_version_counter=False
+        )
+        self.memory_callback = MemoryCallback()
+        self.time_callback = TimeCallback()
+        
+        callbacks = [
+            self.checkpoint_callback,
+            self.memory_callback,
+            self.time_callback
+        ]
+        self.set_callbacks(callbacks)
+
+    def set_callbacks(self, callbacks):
+        # Replace built-in callbacks with custom callbacks
+        custom_callbacks_name = [c.__class__.__name__ for c in callbacks]
+        for c in self.trainer.callbacks:
+            if c.__class__.__name__ in custom_callbacks_name:
+                self.trainer.callbacks.remove(c)
+        for c in callbacks:
+            self.trainer.callbacks.append(c)
+        for c in self.trainer.callbacks:
+            if c.__class__.__name__ == "ModelSummary":
+                self.model_summary_callback = c
+
+    def set_fit_mode(self):
+        self.trainer.logger = TensorBoardLogger(
+            save_dir=f'{self.trainer.default_root_dir}/logs',
+            name=self.tag,
+            version='fit'
+        )
+    
+    def set_test_mode(self):
+        self.trainer.logger = CSVLogger(
+            save_dir=f'{self.trainer.default_root_dir}/logs',
+            name=self.tag,
+            version='test'
+        )
+
+        if not self.model.forecaster.no_training:
+            self.ckpt = self.checkpoint_callback.best_model_path
+            log.info(f"Loading best checkpoint from {self.ckpt}")
+            self.model = ProbTSForecastModule.load_from_checkpoint(
+                self.ckpt, 
+                scaler=self.datamodule.data_manager.scaler,
+                context_length=self.datamodule.data_manager.context_length,
+                target_dim=self.datamodule.data_manager.target_dim,
+                freq=self.datamodule.data_manager.freq,
+                prediction_length=self.datamodule.data_manager.prediction_length,
+                lags_list=self.datamodule.data_manager.lags_list,
+                time_feat_dim=self.datamodule.data_manager.time_feat_dim,
+            )
+
+    def save_exp_summary(self):
+        exp_summary = {}
+        
+        model_summary = self.model_summary_callback._summary(self.trainer, self.model)
+        exp_summary['total_parameters'] = model_summary.total_parameters
+        exp_summary['trainable_parameters'] = model_summary.trainable_parameters
+        exp_summary['model_size'] = model_summary.model_size
+        
+        memory_summary = self.memory_callback.memory_summary
+        exp_summary['memory_summary'] = memory_summary
+        
+        time_summary = self.time_callback.time_summary
+        exp_summary['time_summary'] = time_summary
+        for batch_key, batch_time in time_summary.items():
+            if len(batch_time) > 0:
+                exp_summary[f'mean_{batch_key}'] = sum(batch_time) / len(batch_time)
+        
+        summary_save_path = f"{self.trainer.default_root_dir}/logs/{self.tag}/summary.json"
+        with open(summary_save_path, 'w') as f:
+            json.dump(exp_summary, f, indent=4)
+        log.info(f"Summary saved to {summary_save_path}")
+        
+    def run(self):
+        self.init_exp()
+        
+        if not self.model.forecaster.no_training:
+            self.set_fit_mode()
+            self.trainer.fit(model=self.model, datamodule=self.datamodule)
+
+        self.set_test_mode()
+        self.trainer.test(model=self.model, datamodule=self.datamodule)
+        
+        self.save_exp_summary()
+
+
+if __name__ == '__main__':
+    cli = ProbTSCli(
+        datamodule_class=ProbTSDataModule,
+        model_class=ProbTSForecastModule,
+        save_config_kwargs={"overwrite": True},
+        run=False
+    )
+    cli.run()
\ No newline at end of file
diff --git a/run.sh b/run.sh
new file mode 100644
index 0000000..9b9f2d5
--- /dev/null
+++ b/run.sh
@@ -0,0 +1,34 @@
+export CUDA_VISIBLE_DEVICES=0
+MODEL=patchtst
+DATASET=etth1
+CTX_LEN=96
+PRED_LEN=96
+
+DATA_DIR=/path/to/datasets
+LOG_DIR=/path/to/log_dir
+
+# multivariate datasets:
+# ['exchange_rate_nips', 'solar_nips','electricity_nips', 'traffic_nips','wiki2000_nips']
+
+# Univariate datasets:
+# ['m4_weekly', 'm4_hourly', 'm4_daily', 'm4_monthly', 'm4_quarterly', 'm4_yearly', 'm5', 'tourism_monthly', 'tourism_quarterly', 'tourism_yearly']
+
+# Long-term forecasting:
+# ['etth1', 'etth2','ettm1','ettm2','traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'illness_ltsf', 'weather_ltsf']
+# NOTE: when using long-term forecasting datasets, please explicit assign context_length and prediction_length, e.g., :
+# --data.data_manager.init_args.context_length 96 \
+# --data.data_manager.init_args.prediction_length 192 \
+
+# run pipeline with train and test
+# replace ${MODEL} with tarfet model name, e.g, GRU_NVP
+# replace ${DATASET} with dataset name
+
+# if not specify dataset_path, the default path is ./datasets
+
+python run.py --config config/ltsf/${DATASET}/${MODEL}.yaml --seed_everything 0  \
+    --data.data_manager.init_args.path ${DATA_DIR} \
+    --trainer.default_root_dir ${LOG_DIR} \
+    --data.data_manager.init_args.split_val true \
+    --data.data_manager.init_args.dataset ${DATASET} \
+    --data.data_manager.init_args.context_length ${CTX_LEN} \
+    --data.data_manager.init_args.prediction_length ${PRED_LEN} 
diff --git a/run_tsfm.sh b/run_tsfm.sh
new file mode 100644
index 0000000..399c5ea
--- /dev/null
+++ b/run_tsfm.sh
@@ -0,0 +1,34 @@
+export CUDA_VISIBLE_DEVICES=0
+MODEL=moirai
+DATASET=etth1
+CTX_LEN=96
+PRED_LEN=96
+
+DATA_DIR=/path/to/datasets
+LOG_DIR=/path/to/log_dir
+
+# multivariate datasets:
+# ['exchange_rate_nips', 'solar_nips','electricity_nips', 'traffic_nips','wiki2000_nips']
+
+# Univariate datasets:
+# ['m4_weekly', 'm4_hourly', 'm4_daily', 'm4_monthly', 'm4_quarterly', 'm4_yearly', 'm5', 'tourism_monthly', 'tourism_quarterly', 'tourism_yearly']
+
+# Long-term forecasting:
+# ['etth1', 'etth2','ettm1','ettm2','traffic_ltsf', 'electricity_ltsf', 'exchange_ltsf', 'illness_ltsf', 'weather_ltsf']
+# NOTE: when using long-term forecasting datasets, please explicit assign context_length and prediction_length, e.g., :
+# --data.data_manager.init_args.context_length 96 \
+# --data.data_manager.init_args.prediction_length 192 \
+
+# run pipeline with train and test
+# replace ${MODEL} with tarfet model name, e.g, GRU_NVP
+# replace ${DATASET} with dataset name
+
+# if not specify dataset_path, the default path is ./datasets
+
+python run.py --config config/tsfm/${MODEL}.yaml --seed_everything 0  \
+    --data.data_manager.init_args.path ${DATA_DIR} \
+    --trainer.default_root_dir ${LOG_DIR} \
+    --data.data_manager.init_args.split_val true \
+    --data.data_manager.init_args.context_length ${CTX_LEN} \
+    --data.data_manager.init_args.prediction_length ${PRED_LEN} \
+    --data.data_manager.init_args.dataset ${DATASET} \
diff --git a/submodules/lag_llama b/submodules/lag_llama
new file mode 160000
index 0000000..4ad82d9
--- /dev/null
+++ b/submodules/lag_llama
@@ -0,0 +1 @@
+Subproject commit 4ad82d9d5d6bff4e9dd9c36aa6eba4af7a4d4e07
diff --git a/submodules/timesfm b/submodules/timesfm
new file mode 160000
index 0000000..5c7b905
--- /dev/null
+++ b/submodules/timesfm
@@ -0,0 +1 @@
+Subproject commit 5c7b905e8deccad1a026a9475b00777fbadc0b18
diff --git a/submodules/tsfm b/submodules/tsfm
new file mode 160000
index 0000000..a730b5d
--- /dev/null
+++ b/submodules/tsfm
@@ -0,0 +1 @@
+Subproject commit a730b5d4c511b2fc24a9ac7e5841e1dc3077e893
diff --git a/submodules/uni2ts b/submodules/uni2ts
new file mode 160000
index 0000000..fce6a6f
--- /dev/null
+++ b/submodules/uni2ts
@@ -0,0 +1 @@
+Subproject commit fce6a6f57bc3bc1a57c7feb3abc6c7eb2f264301

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