This repository contains a reading list of papers on time series forecasting/prediction (TSF). These papers are mainly categorized according to the type of model.
Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
---|---|---|---|---|
15-11-23 | Multi-step | ACOMP 2015 | Comparison of Strategies for Multi-step-Ahead Prediction of Time Series Using Neural Network | None |
19-06-20 | DL | SENSJ 2019 | A Review of Deep Learning Models for Time Series Prediction | None |
20-09-27 | DL | Arxiv 2020 | Time Series Forecasting With Deep Learning: A Survey | None |
22-02-15 | Transformer | Arxiv 2022 | Transformers in Time Series: A Survey | None |
23-05-01 | Diffusion | Arxiv 2023 | Diffusion Models for Time Series Applications: A Survey | None |
Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
---|---|---|---|---|
17-03-21 | LSTNet | SIGIR 2018 | Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks | LSTNet |
17-04-07 | DA-RNN | IJCAI 2017 | A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction | DARNN |
17-04-13 | DeepAR | IJoF 2019 | DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks | DeepAR |
17-11-29 | MQRNN | NIPSW 2017 | A Multi-Horizon Quantile Recurrent Forecaster | MQRNN |
18-06-23 | mWDN | KDD 2018 | Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis | mWDN |
18-09-06 | MTNet | AAAI 2019 | A Memory-Network Based Solution for Multivariate Time-Series Forecasting | MTNet |
19-05-28 | DF-Model | ICML 2019 | Deep Factors for Forecasting | None |
19-07-01 | MH-RNN | KDD 2019 | Multi-Horizon Time Series Forecasting with Temporal Attention Learning | None |
19-07-18 | ESLSTM | IJoF 2020 | A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting | None |
19-07-25 | MH-TAL | KDD 2019 | Multi-Horizon Time Series Forecasting with Temporal Attention Learning | None |
22-05-16 | C2FAR | NIPS 2022 | C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting | C2FAR |
Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
---|---|---|---|---|
17-05-25 | ND | TNNLS 2017 | Neural Decomposition of Time-Series Data for Effective Generalization | None |
19-05-24 | NBeats | ICLR 2020 | N-BEATS: Neural Basis Expansion Analysis For Interpretable Time Series Forecasting | NBeats |
21-04-12 | NBeatsX | IJoF 2022 | Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx | NBeatsX |
22-01-30 | N-HiTS | AAAI 2023 | N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting | N-HiTS |
22-05-15 | DEPTS | ICLR 2022 | DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting | DEPTS |
22-05-24 | FreDo | Arxiv 2022 | FreDo: Frequency Domain-based Long-Term Time Series Forecasting | None |
22-05-26 | DLinear | AAAI 2023 | Are Transformers Effective for Time Series Forecasting? | DLinear |
22-06-24 | TreeDRNet | Arxiv 2022 | TreeDRNet: A Robust Deep Model for Long Term Time Series Forecasting | None |
22-07-04 | LightTS | Arxiv 2022 | Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures | LightTS |
23-02-09 | MTS-Mixers | Arxiv 2023 | MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing | MTS-Mixers |
23-03-10 | TSMixer | Arxiv 2023 | TSMixer: An all-MLP Architecture for Time Series Forecasting | None |
23-04-17 | TiDE | Arxiv 2023 | Long-term Forecasting with TiDE: Time-series Dense Encoder | TiDE |
23-05-18 | RTSF | Arxiv 2023 | Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping | RTSF |
Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
---|---|---|---|---|
19-05-09 | DeepGLO | NIPS 2019 | Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting | deepglo |
19-05-22 | DSANet | CIKM 2019 | DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting | DSANet |
19-12-11 | MLCNN | AAAI 2020 | Towards Better Forecasting by Fusing Near and Distant Future Visions | MLCNN |
21-06-17 | SCINet | NIPS 2022 | SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction | SCINet |
22-09-22 | MICN | ICLR 2023 | MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting | MICN |
22-09-22 | TimesNet | ICLR 2023 | TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis | TimesNet |
23-02-23 | LightCTS | SIGMOD 2023 | LightCTS: A Lightweight Framework for Correlated Time Series Forecasting | LightCTS |
23-05-25 | TLNets | Arxiv 2023 | TLNets: Transformation Learning Networks for long-range time-series prediction | TLNets |
23-06-04 | Cross-LKTCN | Arxiv 2023 | Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting | None |
Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
---|---|---|---|---|
18-05-18 | DSSM | NIPS 2018 | Deep State Space Models for Time Series Forecasting | None |
22-08-19 | SSSD | TMLR 2022 | Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models | SSSD |
22-09-22 | SpaceTime | ICLR 2023 | Effectively Modeling Time Series with Simple Discrete State Spaces | SpaceTime |
22-12-24 | LS4 | Arxiv 2022 | Deep Latent State Space Models for Time-Series Generation | None |
Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
---|---|---|---|---|
Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
---|---|---|---|---|
20-02-14 | MAF | ICLR 2021 | Multivariate Probabilitic Time Series Forecasting via Conditioned Normalizing Flows | MAF |
21-01-18 | TimeGrad | ICML 2021 | Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting | TimeGrad |
21-07-07 | CSDI | NIPS 2021 | CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation | CSDI |
22-05-16 | MANF | Arxiv 2022 | Multi-scale Attention Flow for Probabilistic Time Series Forecasting | None |
22-05-16 | D3VAE | NIPS 2022 | Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement | D3VAE |
22-05-16 | LaST | NIPS 2022 | LaST: Learning Latent Seasonal-Trend Representations for Time Series Forecasting | LaST |
22-12-28 | Hier-Transformer-CNF | Arxiv 2022 | End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation | None |
23-03-13 | HyVAE | Arxiv 2023 | Hybrid Variational Autoencoder for Time Series Forecasting | None |
Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
---|---|---|---|---|
19-02-21 | DAIN | TNNLS 2020 | Deep Adaptive Input Normalization for Time Series Forecasting | DAIN |
19-09-19 | DILATE | NIPS 2019 | Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models | DILATE |
21-07-19 | TAN | NIPS 2021 | Topological Attention for Time Series Forecasting | TAN |
21-09-29 | RevIN | ICLR 2022 | Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift | RevIN |
22-02-23 | MQF2 | AISTATS 2022 | Multivariate Quantile Function Forecaster | None |
22-05-18 | FiLM | NIPS 2022 | FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting | FiLM |
23-02-18 | FrAug | Arxiv 2023 | FrAug: Frequency Domain Augmentation for Time Series Forecasting | FrAug |
23-02-22 | Dish-TS | AAAI 2023 | Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting | Dish-TS |
23-02-23 | Adaptive Sampling | NIPSW 2022 | Adaptive Sampling for Probabilistic Forecasting under Distribution Shift | None |
Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
---|---|---|---|---|
23-02-23 | FPT | Arxiv 2023 | Power Time Series Forecasting by Pretrained LM | FPT |
Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
---|---|---|---|---|
22-10-25 | WaveBound | NIPS 2022 | WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting | None |
23-05-25 | Ensembling | ICML 2023 | Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting | None |
Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
---|---|---|---|---|
16-12-05 | TRMF | NIPS 2016 | Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction | TRMF |
17-08-25 | Prophet | TAS 2018 | Forecasting at Scale | Prophet |