Release 0.25.0 (#1935)

* update changelog

* move unreleased section to release 0.25.0 in changelog

* bump u8darts 0.24.0

Author: dennisbader

CHANGELOG.md

@@ -6,45 +6,48 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 ## [Unreleased](https://github.com/unit8co/darts/tree/master)
 
-[Full Changelog](https://github.com/unit8co/darts/compare/0.24.0...master)
+[Full Changelog](https://github.com/unit8co/darts/compare/0.25.0...master)
 
+## [0.25.0](https://github.com/unit8co/darts/tree/0.25.0) (2023-08-04)
 ### For users of the library:
 
 **Installation**
 - 🔴 Removed Prophet, LightGBM, and CatBoost dependencies from PyPI packages (`darts`, `u8darts`, `u8darts[torch]`), and conda-forge packages (`u8darts`, `u8darts-torch`)  to avoid installation issues that some users were facing (installation on Apple M1/M2 devices, ...). [#1589](https://github.com/unit8co/darts/pull/1589) by [Julien Herzen](https://github.com/hrzn) and [Dennis Bader](https://github.com/dennisbader).
   - The models are still supported by installing the required packages as described in our [installation guide](https://github.com/unit8co/darts/blob/master/INSTALL.md#enabling-optional-dependencies).
-  - PyPi package `u8darts[all]` and conda-forge package `u8darts-all` are now equivalent to the old `darts` package (all dependencies). 
-  - Added new PyPI flavor `u8darts[notorch]`, and conda-forge flavor `u8darts-notorch` which are equivalent to the old `u8darts` installation.
-- 🔴 Removed support for Python 3.7 [#1864](https://github.com/unit8co/darts/pull/#1864) by [Dennis Bader](https://github.com/dennisbader).
+  - The Darts package including all dependencies can still be installed with PyPI package `u8darts[all]` or conda-forge package `u8darts-all`. 
+  - Added new PyPI flavor `u8darts[notorch]`, and conda-forge flavor `u8darts-notorch` which are equivalent to the old `u8darts` installation (all dependencies except neural networks).
+- 🔴 Removed support for Python 3.7 [#1864](https://github.com/unit8co/darts/pull/1864) by [Dennis Bader](https://github.com/dennisbader).
 
 **Improved**
 - General model improvements:
-  - Added support for `PathLike` to the `save()` and `load()` functions of all non-deep learning based models. [#1754](https://github.com/unit8co/darts/pull/1754) by [Simon Sudrich](https://github.com/sudrich).
+  - 🚀🚀 Optimized `historical_forecasts()` for `RegressionModel` when `retrain=False` and `forecast_horizon <= output_chunk_length` by vectorizing the prediction. This can run up to 700 times faster than before! [#1885](https://github.com/unit8co/darts/pull/1885) by [Antoine Madrona](https://github.com/madtoinou).
   - Improved efficiency of `historical_forecasts()` and `backtest()` for all models giving significant process time reduction for larger number of predict iterations and series. [#1801](https://github.com/unit8co/darts/pull/1801) by [Dennis Bader](https://github.com/dennisbader).
-  - Optimized `historical_forecasts()` for `RegressionModel` when `retrain=False` and `forecast_horizon <= output_chunk_length` by vectorizing the prediction. [#1885](https://github.com/unit8co/darts/pull/1885) by [Antoine Madrona](https://github.com/madtoinou).
-  - Added model property `ForecastingModel.supports_multivariate` to indicate whether the model supports multivariate forecasting. [#1848](https://github.com/unit8co/darts/pull/1848) by [Felix Divo](https://github.com/felixdivo).
-  - `Prophet` now supports conditional seasonalities, and properly handles all parameters passed to `Prophet.add_seasonality()` and model creation parameter `add_seasonalities` [#1829](https://github.com/unit8co/darts/pull/#1829) by [Idan Shilon](https://github.com/id5h).
-  - Added support for direct prediction of the likelihood parameters to probabilistic models using a likelihood (regression and torch models). Set `predict_likelihood_parameters=True` when calling `predict()`. [#1811](https://github.com/unit8co/darts/pull/1811) by [Antoine Madrona](https://github.com/madtoinou).
+  - 🚀🚀 Added support for direct prediction of the likelihood parameters to probabilistic models using a likelihood (regression and torch models). Set `predict_likelihood_parameters=True` when calling `predict()`. [#1811](https://github.com/unit8co/darts/pull/1811) by [Antoine Madrona](https://github.com/madtoinou).
+  - 🚀🚀 New forecasting model: `TiDEModel`  as proposed in [this paper](https://arxiv.org/abs/2304.08424). An MLP based encoder-decoder model that is said to outperform many Transformer-based architectures. [#1727](https://github.com/unit8co/darts/pull/1727) by [Alex Colpitts](https://github.com/alexcolpitts96).
+  - `Prophet` now supports conditional seasonalities, and properly handles all parameters passed to `Prophet.add_seasonality()` and model creation parameter `add_seasonalities` [#1829](https://github.com/unit8co/darts/pull/1829) by [Idan Shilon](https://github.com/id5h).
   - Added method `generate_fit_predict_encodings()` to generate the encodings (from `add_encoders` at model creation) required for training and prediction. [#1925](https://github.com/unit8co/darts/pull/1925) by [Dennis Bader](https://github.com/dennisbader).
+  - Added support for `PathLike` to the `save()` and `load()` functions of all non-deep learning based models. [#1754](https://github.com/unit8co/darts/pull/1754) by [Simon Sudrich](https://github.com/sudrich).
+  - Added model property `ForecastingModel.supports_multivariate` to indicate whether the model supports multivariate forecasting. [#1848](https://github.com/unit8co/darts/pull/1848) by [Felix Divo](https://github.com/felixdivo).
 - Improvements to `EnsembleModel`:
   - Model creation parameter `forecasting_models` now supports a mix of `LocalForecastingModel` and `GlobalForecastingModel` (single `TimeSeries` training/inference only, due to the local models). [#1745](https://github.com/unit8co/darts/pull/1745) by [Antoine Madrona](https://github.com/madtoinou).
   - Future and past covariates can now be used even if `forecasting_models` have different covariates support. The covariates passed to `fit()`/`predict()` are used only by models that support it. [#1745](https://github.com/unit8co/darts/pull/1745) by [Antoine Madrona](https://github.com/madtoinou).
-  - `RegressionEnsembleModel` and `NaiveEnsembleModel` can generate probabilistic forecasts, probabilistics `forecasting_models` can be sampled to train the `regression_model`, updated the documentation (stacking technique). [#1692](https://github.com/unit8co/darts/pull/#1692) by [Antoine Madrona](https://github.com/madtoinou).
-- Improvements to `ShapExplainer`:
-  - Added static covariates support to `ShapeExplainer`. [#1803](https://github.com/unit8co/darts/pull/#1803) by [Anne de Vries](https://github.com/anne-devries) and [Dennis Bader](https://github.com/dennisbader).
-  - Improved static covariates column naming when applying a `sklearn.preprocessing.OneHotEncoder` with `StaticCovariatesTransformer` [#1863](https://github.com/unit8co/darts/pull/1863) by [Anne de Vries](https://github.com/anne-devries).
-- Added `MSTL` (Season-Trend decomposition using LOESS for multiple seasonalities) as a `method` option for `extract_trend_and_seasonality()`. [#1879](https://github.com/unit8co/darts/pull/1879) by [Alex Colpitts](https://github.com/alexcolpitts96).
-- Added `RINorm` (Reversible Instance Norm) as a new layer normalization option. [#1121](https://github.com/unit8co/darts/issues/1121) by [Alex Colpitts](https://github.com/alexcolpitts96).
-- New forecasting model: `TiDEModel`  as proposed in [this paper](https://arxiv.org/abs/2304.08424). An MLP based encoder-decoder model that outperforms many Transformer-based architectures. [#1727](https://github.com/unit8co/darts/pull/1727) by [Alex Colpitts](https://github.com/alexcolpitts96).
-- New forecasting model explainer: `TFTExplainer` for `TFTModel`. You can now access and visualize the trained model's feature importances and self attention. [#1392](https://github.com/unit8co/darts/issues/1392) by [Sebastian Cattes](https://github.com/Cattes) and [Dennis Bader](https://github.com/dennisbader).
-- Improvements to `TimeSeries.plot()`: custom axes are now properly supported with parameter `ax`. Axis is now returned for downstream tasks. [#1916](https://github.com/unit8co/darts/pull/1916) by [Dennis Bader](https://github.com/dennisbader).
+  - `RegressionEnsembleModel` and `NaiveEnsembleModel` can generate probabilistic forecasts, probabilistics `forecasting_models` can be sampled to train the `regression_model`, updated the documentation (stacking technique). [#1692](https://github.com/unit8co/darts/pull/1692) by [Antoine Madrona](https://github.com/madtoinou).
+- Improvements to `Explainability` module:
+  - 🚀🚀 New forecasting model explainer: `TFTExplainer` for `TFTModel`. You can now access and visualize the trained model's feature importances and self attention. [#1392](https://github.com/unit8co/darts/issues/1392) by [Sebastian Cattes](https://github.com/Cattes) and [Dennis Bader](https://github.com/dennisbader).
+  - Added static covariates support to `ShapeExplainer`. [#1803](https://github.com/unit8co/darts/pull/1803) by [Anne de Vries](https://github.com/anne-devries) and [Dennis Bader](https://github.com/dennisbader).
+- Other improvements:
+  - Improved static covariates column naming when using `StaticCovariatesTransformer` with a `sklearn.preprocessing.OneHotEncoder`. [#1863](https://github.com/unit8co/darts/pull/1863) by [Anne de Vries](https://github.com/anne-devries).
+  - Added `MSTL` (Season-Trend decomposition using LOESS for multiple seasonalities) as a `method` option for `extract_trend_and_seasonality()`. [#1879](https://github.com/unit8co/darts/pull/1879) by [Alex Colpitts](https://github.com/alexcolpitts96).
+  - Added `RINorm` (Reversible Instance Norm) as a new input normalization option for `TorchForecastingModel`. So far only `TiDEModel` supports it with model creation parameter `user_reversible_instance_norm`. [#1865](https://github.com/unit8co/darts/issues/1856) by [Alex Colpitts](https://github.com/alexcolpitts96).
+  - Improvements to `TimeSeries.plot()`: custom axes are now properly supported with parameter `ax`. Axis is now returned for downstream tasks. [#1916](https://github.com/unit8co/darts/pull/1916) by [Dennis Bader](https://github.com/dennisbader).
 
 **Fixed**
 - Fixed an issue not considering original component names for `TimeSeries.plot()` when providing a label prefix. [#1783](https://github.com/unit8co/darts/pull/1783) by [Simon Sudrich](https://github.com/sudrich).
-- Fixed an issue with `TorchForecastingModel.load_from_checkpoint()` not properly loading the loss function and metrics. [#1749](https://github.com/unit8co/darts/pull/1749) by [Antoine Madrona](https://github.com/madtoinou).
+- Fixed an issue with the string representation of `ForecastingModel` when using array-likes at model creation. [#1749](https://github.com/unit8co/darts/pull/1749) by [Antoine Madrona](https://github.com/madtoinou).
+- Fixed an issue with `TorchForecastingModel.load_from_checkpoint()` not properly loading the loss function and metrics. [#1759](https://github.com/unit8co/darts/pull/1759) by [Antoine Madrona](https://github.com/madtoinou).
 - Fixed a bug when loading the weights of a `TorchForecastingModel` trained with encoders or a Likelihood. [#1744](https://github.com/unit8co/darts/pull/1744) by [Antoine Madrona](https://github.com/madtoinou).
-- Fixed a bug when using selected `target_components` with `ShapExplainer. [#1803](https://github.com/unit8co/darts/pull/#1803) by [Dennis Bader](https://github.com/dennisbader).
-- Fixed `TimeSeries.__getitem__()` for series with a RangeIndex with start != 0 and freq != 1. [#1868](https://github.com/unit8co/darts/pull/#1868) by [Dennis Bader](https://github.com/dennisbader).
+- Fixed a bug when using selected `target_components` with `ShapExplainer`. [#1803](https://github.com/unit8co/darts/pull/1803) by [Dennis Bader](https://github.com/dennisbader).
+- Fixed `TimeSeries.__getitem__()` for series with a RangeIndex with start != 0 and freq != 1. [#1868](https://github.com/unit8co/darts/pull/1868) by [Dennis Bader](https://github.com/dennisbader).
 - Fixed an issue where `DTWAlignment.plot_alignment()` was not plotting the alignment plot of series with a RangeIndex correctly. [#1880](https://github.com/unit8co/darts/pull/1880) by [Ahmet Zamanis](https://github.com/AhmetZamanis) and [Dennis Bader](https://github.com/dennisbader).
 - Fixed an issue when calling `ARIMA.predict()` and `num_samples > 1` (probabilistic forecasting), where the start point of the simulation was not anchored to the end of the target series. [#1893](https://github.com/unit8co/darts/pull/1893) by [Dennis Bader](https://github.com/dennisbader).
 - Fixed an issue when using `TFTModel.predict()` with `full_attention=True` where the attention mask was not applied properly. [#1392](https://github.com/unit8co/darts/issues/1392) by [Dennis Bader](https://github.com/dennisbader).
@@ -53,7 +56,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Improvements**
 - Refactored the `ForecastingModelExplainer` and `ExplainabilityResult` to simplify implementation of new explainers. [#1392](https://github.com/unit8co/darts/issues/1392) by [Dennis Bader](https://github.com/dennisbader).
-- Adapted all unit tests to run successfully on M1 devices. [#1933](https://github.com/unit8co/darts/issues/1393) by [Dennis Bader](https://github.com/dennisbader).
+- Adapted all unit tests to run successfully on M1 devices. [#1933](https://github.com/unit8co/darts/issues/1933) by [Dennis Bader](https://github.com/dennisbader).
 
 ## [0.24.0](https://github.com/unit8co/darts/tree/0.24.0) (2023-04-12)
 ### For users of the library:
@@ -110,8 +113,6 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 - Major refactor of data transformers which simplifies implementation of new transformers. [#1409](https://github.com/unit8co/darts/pull/1409) by [Matt Bilton](https://github.com/mabilton).
 
 
-- Added new `TFTExplainer` class to implement the Explainable AI part described in [the paper](https://arxiv.org/abs/1912.09363) of the `TFT` model. [#1392](https://github.com/unit8co/darts/pull/1392) by [Sebastian Cattes](https://github.com/cattes).
-
 ## [0.23.1](https://github.com/unit8co/darts/tree/0.23.1) (2023-01-12)
 Patch release
 
@@ -130,6 +131,7 @@ Patch release
 - Fix an issue with `slice_n_points` functions on integer indexes.
   [#1482](https://github.com/unit8co/darts/pull/1482) by [Julien Herzen](https://github.com/hrzn).
 
+
 ## [0.23.0](https://github.com/unit8co/darts/tree/0.23.0) (2022-12-23)
 ### For users of the library:
 

setup_u8darts.py

@@ -29,7 +29,7 @@ def read_requirements(path):
 
 setup(
     name="u8darts",
-    version="0.24.0",
+    version="0.25.0",
     description="A python library for easy manipulation and forecasting of time series.",
     long_description=LONG_DESCRIPTION,
     long_description_content_type="text/markdown",