Release 0.22.0 (#1258)

* update changelog

* bump u8darts

* update changelog

* bump u8darts

* update changelog

* Fix a small typo in changelog

* update readme

Author: hrzn

CHANGELOG.md

@@ -4,15 +4,35 @@
 Darts is still in an early development phase, and we cannot always guarantee backwards compatibility. Changes that may **break code which uses a previous release of Darts** are marked with a "🔴".
 
 ## [Unreleased](https://github.com/unit8co/darts/tree/master)
-- Added support for retraining model(s) every `n` iteration and on custom condition in `historical_forecasts` method of `ForecastingModel` abstract class. Addressed issues [#135](https://github.com/unit8co/darts/issues/135) and [#623](https://github.com/unit8co/darts/issues/623) by [Francesco Bruzzesi](https://github.com/fbruzzesi).
+
+[Full Changelog](https://github.com/unit8co/darts/compare/0.22.0...master)
+
+
+## [0.22.0](https://github.com/unit8co/darts/tree/0.22.0) (2022-10-04)
+### For users of the library:
+
+**Improved**
+- New explainability feature. The class `ShapExplainer` in `darts.explainability` can provide Shap-values explanations of the importance of each lag and each dimension in producing each forecasting lag for `RegressionModel`s. [#909](https://github.com/unit8co/darts/pull/909) by [Maxime Dumonal](https://github.com/dumjax).
+- New model: `StatsForecastsETS`. Similarly to `StatsForecastsAutoARIMA`, this model offers the ETS model from Nixtla's `statsforecasts` library as a local forecasting model supporting future covariates. [#1171](https://github.com/unit8co/darts/pull/1171) by [Julien Herzen](https://github.com/hrzn).
+- Added support for past and future covariates to `residuals()` function. [#1223](https://github.com/unit8co/darts/pull/1223) by [Eliane Maalouf](https://github.com/eliane-maalouf).
+- Added support for retraining model(s) every `n` iteration and on custom conditions in `historical_forecasts` method of `ForecastingModel`s. [#1139](https://github.com/unit8co/darts/pull/1139) by [Francesco Bruzzesi](https://github.com/fbruzzesi).
+- Added support for beta-NLL in `GaussianLikelihood`s, as proposed in [this paper](https://arxiv.org/abs/2203.09168). [#1162](https://github.com/unit8co/darts/pull/1162) by [Julien Herzen](https://github.com/hrzn).
 - New LayerNorm alternatives, RMSNorm and LayerNormNoBias [#1113](https://github.com/unit8co/darts/issues/1113) by [Greg DeVos](https://github.com/gdevos010).
-- Fixed type hinting for ExponentialSmoothing model [#1185](https://https://github.com/unit8co/darts/pull/1185) by [Rijk van der Meulen](https://github.com/rijkvandermeulen)
+- 🔴 Improvements to encoders: improve fitting behavior of encoders' transformers and solve a couple of issues. Remove support for absolute index encoding. [#1257](https://github.com/unit8co/darts/pull/1257) by [Dennis Bader](https://github.com/dennisbader).
 - Overwrite min_train_series_length for Catboost and LightGBM [#1214](https://https://github.com/unit8co/darts/pull/1214) by [Anne de Vries](https://github.com/anne-devries).
+- New example notebook showcasing and end-to-end example of hyperparameter optimization with Optuna [#1242](https://github.com/unit8co/darts/pull/1242) by [Julien Herzen](https://github.com/hrzn).
+- New user guide section on hyperparameter optimization with Optuna and Ray Tune [#1242](https://github.com/unit8co/darts/pull/1242) by [Julien Herzen](https://github.com/hrzn).
+- Documentation on model saving and loading. [#1210](https://github.com/unit8co/darts/pull/1210) by [Amadej Kocbek](https://github.com/amadejkocbek).
 - 🔴 `torch_device_str` has been removed from all torch models in favor of Pytorch Lightning's `pl_trainer_kwargs` method [#1244](https://github.com/unit8co/darts/pull/1244) by [Greg DeVos](https://github.com/gdevos010).
 
-
-[Full Changelog](https://github.com/unit8co/darts/compare/0.21.0...master)
-
+**Fixed**
+- An issue with `add_encoders` in `RegressionModel`s when fit/predict were called with a single target series. [#1193](https://github.com/unit8co/darts/pull/1193) by [Dennis Bader](https://github.com/dennisbader).
+- Some issues with integer-indexed series. [#1191](https://github.com/unit8co/darts/pull/1191) by [Julien Herzen](https://github.com/hrzn).
+- A bug when using the latest versions (>=1.1.1) of Prophet. [#1208](https://github.com/unit8co/darts/pull/1208) by [Julien Herzen](https://github.com/hrzn).
+- An issue with calling `fit_transform()` on reconciliators. [#1165](https://github.com/unit8co/darts/pull/1165) by [Julien Herzen](https://github.com/hrzn).
+- A bug in `GaussianLikelihood` object causing issues with confidence intervals. [#1162](https://github.com/unit8co/darts/pull/1162) by [Julien Herzen](https://github.com/hrzn).
+- An issue which prevented plotting `TimeSeries` of length 1. [#1206](https://github.com/unit8co/darts/issues/1206) by [Julien Herzen](https://github.com/hrzn).
+- Type hinting for ExponentialSmoothing model [#1185](https://https://github.com/unit8co/darts/pull/1185) by [Rijk van der Meulen](https://github.com/rijkvandermeulen)
 
 ## [0.21.0](https://github.com/unit8co/darts/tree/0.21.0) (2022-08-12)
 

README.md

@@ -39,10 +39,11 @@ series, and some of the models offer a rich support for probabilistic forecastin
 * [Temporal Convolutional Networks and Forecasting](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4)
 * [Probabilistic Forecasting](https://medium.com/unit8-machine-learning-publication/probabilistic-forecasting-in-darts-e88fbe83344e)
 * [Transfer Learning for Time Series Forecasting](https://medium.com/unit8-machine-learning-publication/transfer-learning-for-time-series-forecasting-87f39e375278)
+* [Hierarchical Forecast Reconciliation](https://medium.com/unit8-machine-learning-publication/hierarchical-forecast-reconciliation-with-darts-8b4b058bb543)
 
 ## Quick Install
 
-We recommend to first setup a clean Python environment for your project with at least Python 3.7 using your favorite tool
+We recommend to first setup a clean Python environment for your project with Python 3.7+ using your favorite tool
 ([conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html "conda-env"),
 [venv](https://docs.python.org/3/library/venv.html), [virtualenv](https://virtualenv.pypa.io/en/latest/) with
 or without [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)).
@@ -63,10 +64,10 @@ import pandas as pd
 from darts import TimeSeries
 
 # Read a pandas DataFrame
-df = pd.read_csv('AirPassengers.csv', delimiter=",")
+df = pd.read_csv("AirPassengers.csv", delimiter=",")
 
 # Create a TimeSeries, specifying the time and value columns
-series = TimeSeries.from_dataframe(df, 'Month', '#Passengers')
+series = TimeSeries.from_dataframe(df, "Month", "#Passengers")
 
 # Set aside the last 36 months as a validation series
 train, val = series[:-36], series[-36:]
@@ -86,7 +87,7 @@ Plot the median, 5th and 95th percentiles:
 import matplotlib.pyplot as plt
 
 series.plot()
-prediction.plot(label='forecast', low_quantile=0.05, high_quantile=0.95)
+prediction.plot(label="forecast", low_quantile=0.05, high_quantile=0.95)
 plt.legend()
 ```
 
@@ -112,6 +113,7 @@ plt.legend()
   These can make the forecasts add up in a way that respects the underlying hierarchy.
 * **Regression Models:** It is possible to plug-in any scikit-learn compatible model
   to obtain forecasts as functions of lagged values of the target series and covariates.
+* **Explainability:** Darts has the ability to *explain* forecasting models by using Shap values.
 * **Data processing:** Tools to easily apply (and revert) common transformations on
   time series data (scaling, filling missing values, boxcox, ...)
 * **Metrics:** A variety of metrics for evaluating time series' goodness of fit;
@@ -134,8 +136,9 @@ Model | Univariate | Multivariate | Probabilistic | Multiple-series training | P
 `ARIMA` | ✅ | | ✅ | | | ✅ | |
 `VARIMA` | ✅ | ✅ | | | | ✅ | |
 `AutoARIMA` | ✅ | | | | | ✅ | |
-`StatsForecastAutoARIMA` (faster AutoARIMA) | ✅ | | ✅ | | | ✅ | | [statsforecast](https://github.com/Nixtla/statsforecast)
+`StatsForecastAutoARIMA` (faster AutoARIMA) | ✅ | | ✅ | | | ✅ | | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast)
 `ExponentialSmoothing` | ✅ | | ✅ | | | | |
+`StatsForecastETS` | ✅ | | | | | ✅ | | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast)
 `BATS` and `TBATS` | ✅ | | ✅ | | | | | [TBATS paper](https://robjhyndman.com/papers/ComplexSeasonality.pdf)
 `Theta` and `FourTheta` | ✅ | | | | | | | [Theta](https://robjhyndman.com/papers/Theta.pdf) & [4 Theta](https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R)
 `Prophet` (see [install notes](https://github.com/unit8co/darts/blob/master/INSTALL.md#enabling-support-for-facebook-prophet)) | ✅ | | ✅ | | | ✅ | | [Prophet repo](https://github.com/facebook/prophet)

setup_u8darts.py

@@ -28,7 +28,7 @@ def read_requirements(path):
 
 setup(
     name="u8darts",
-    version="0.21.0",
+    version="0.22.0",
     description="A python library for easy manipulation and forecasting of time series.",
     long_description=LONG_DESCRIPTION,
     long_description_content_type="text/markdown",