From 9f83942fa0c2dce35ac172cabc9ad9863782b60f Mon Sep 17 00:00:00 2001 From: Julien Herzen Date: Wed, 14 Apr 2021 10:42:17 +0200 Subject: [PATCH] update changelog --- CHANGELOG.md | 39 ++++++++++++++++++++++++++++++++++++--- 1 file changed, 36 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 090306cd53..46d6394719 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,7 +4,40 @@ Darts is still in an early development phase and we cannot always guarantee back ## [Unreleased](https://github.com/unit8co/darts/tree/develop) -[Full Changelog](https://github.com/unit8co/darts/compare/0.6.0...develop) +[Full Changelog](https://github.com/unit8co/darts/compare/0.7.0...develop) + + +## [0.7.0](https://github.com/unit8co/darts/tree/0.7.0) (2021-02-02) + +[Full Changelog](https://github.com/unit8co/darts/compare/0.6.0...0.7.0) +### For users of the library: + +**Added:** +- `darts` Pypi package. It is now possible to `pip install darts`. The older name `u8darts` is still maintained +and provides the different flavours for lighter installs. +- New forecasting model available: VARIMA (Vector Autoregressive moving average). +- Support for exogeneous variables in ARIMA, AutoARIMA and VARIMA (optional `exog` parameter in `fit()` and `predict()` +methods). +- New argument `dummy_index` for `TimeSeries` creation. If a series is just composed of a sequence of numbers +without timestamps, setting this flag will allow to create a `TimeSeries` which uses a "dummy time index" behind the +scenes. This simplifies the creation of `TimeSeries` in such cases, and makes it possible to use all forecasting models, +except those that explicitly rely on dates. +- New method `TimeSeries.diff()` returning differenced `TimeSeries`. +- Added an example of `RegressionEnsembleModel` in intro notebook. + +**Changed:** +- Improved N-BEATS example notebook. +- Methods `TimeSeries.split_before()` and `split_after()` now also accept integer or float arguments (in addition to +timestamp) for the breaking point (e.g. specify 0.8 in order to obtain a 80%/20% split). +- Argument `value_cols` no longer has to be provided if not necessary when creating a `TimeSeries` from a `DataFrame`. +- Update of dependency requirements to more recent versions. + +**Fixed:** +- Fix issue with MAX_TORCH_SEED_VALUE on 32-bit architectures (https://github.com/unit8co/darts/issues/235). +- Corrected a bug in TCN inference, which should improve accuracy. +- Fix historical forecasts not returning last point. +- Fixed bug when calling the `TimeSeries.gaps()` function for non-regular time frequencies. +- Many small bug fixes. ## [0.6.0](https://github.com/unit8co/darts/tree/0.6.0) (2021-02-02) @@ -30,9 +63,9 @@ several time series. https://github.com/unit8co/darts/blob/master/examples/02-multi-time-series-and-covariates.ipynb **Changed:** -🔴 removed the arguments `training_series` and `target_series` in `ForecastingModel`s. Please consult +- 🔴 removed the arguments `training_series` and `target_series` in `ForecastingModel`s. Please consult the API documentation of forecasting models to see the new signatures. -🔴 removed `UnivariateForecastingModel` and `MultivariateForecastingModel` base classes. This distinction does +- 🔴 removed `UnivariateForecastingModel` and `MultivariateForecastingModel` base classes. This distinction does not exist anymore. Instead, now some models are "global" (can be trained on multiple series) or "local" (they cannot). All implementations of `GlobalForecastingModel`s support multivariate time series out of the box, except N-BEATS. - Improved the documentation and README.