prepare release 0.20.0 (#1025)

* prepare release 0.20.0

* Improve readme

* Improve readme

* improve readme

* Improve readme

CHANGELOG.md

@@ -4,19 +4,41 @@
 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)
-[Full Changelog](https://github.com/unit8co/darts/compare/0.19.0...master)
+[Full Changelog](https://github.com/unit8co/darts/compare/0.20.0...master)
+
+
+## [0.20.0](https://github.com/unit8co/darts/tree/0.20.0) (2022-06-22)
+
 ### For users of the library:
 
 **Improved**
-- Option to avoid global matplotlib configuration changes.
-  [#924](https://github.com/unit8co/darts/pull/924) by [Mike Richman](https://github.com/zgana).
-- Model Improvements: Option for changing activation function for NHiTs and NBEATS. NBEATS support for dropout. NHiTs Support for AvgPooling1d. [#955](https://github.com/unit8co/darts/pull/955) by [Greg DeVos](https://github.com/gdevos010)
+- Added support for static covariates in `TimeSeries` class. [#966](https://github.com/unit8co/darts/pull/966) by [Dennis Bader](https://github.com/dennisbader).
+- Added support for static covariates in TFT model. [#966](https://github.com/unit8co/darts/pull/966) by [Dennis Bader](https://github.com/dennisbader).
+- Support for storing hierarchy of components in `TimeSeries` (in view of hierarchical reconciliation) [#1012](https://github.com/unit8co/darts/pull/1012) by [Julien Herzen](https://github.com/hrzn).
+- New Reconciliation transformers for forececast reconciliation: bottom up, top down and MinT. [#1012](https://github.com/unit8co/darts/pull/1012) by [Julien Herzen](https://github.com/hrzn).
+- Added support for Monte Carlo Dropout, as a way to capture model uncertainty with torch models at inference time. [#1013](https://github.com/unit8co/darts/pull/1013) by [Julien Herzen](https://github.com/hrzn).
 - New datasets: ETT and Electricity. [#617](https://github.com/unit8co/darts/pull/617)
   by [Greg DeVos](https://github.com/gdevos010)
-- Implemented ["GLU Variants Improve Transformer"](https://arxiv.org/abs/2002.05202) for transformer based models (transformer and TFT). [#959](https://github.com/unit8co/darts/issues/959)
-  by [Greg DeVos](https://github.com/gdevos010) 
-- Added support for torch metrics during training and validation. [#996](https://github.com/unit8co/darts/pull/996) by [Greg DeVos](https://github.com/gdevos010)
-- New dataset: [Uber TLC](https://github.com/fivethirtyeight/uber-tlc-foil-response). [#1003](https://github.com/unit8co/darts/pull/1003) by [Greg DeVos](https://github.com/gdevos010) 
+- New dataset: [Uber TLC](https://github.com/fivethirtyeight/uber-tlc-foil-response). [#1003](https://github.com/unit8co/darts/pull/1003) by [Greg DeVos](https://github.com/gdevos010).
+- Model Improvements: Option for changing activation function for NHiTs and NBEATS. NBEATS support for dropout. NHiTs Support for AvgPooling1d. [#955](https://github.com/unit8co/darts/pull/955) by [Greg DeVos](https://github.com/gdevos010).
+- Implemented ["GLU Variants Improve Transformer"](https://arxiv.org/abs/2002.05202) for transformer based models (transformer and TFT). [#959](https://github.com/unit8co/darts/issues/959) by [Greg DeVos](https://github.com/gdevos010).
+- Added support for torch metrics during training and validation. [#996](https://github.com/unit8co/darts/pull/996) by [Greg DeVos](https://github.com/gdevos010).
+- Better handling of logging [#1010](https://github.com/unit8co/darts/pull/1010) by [Dustin Brunner](https://github.com/brunnedu).
+- Better support for Python 3.10, and dropping `prophet` as a dependency (`Prophet` model still works if `prophet` package is installed separately) [#1023](https://github.com/unit8co/darts/pull/1023) by [Julien Herzen](https://github.com/hrzn).
+- Option to avoid global matplotlib configuration changes.
+[#924](https://github.com/unit8co/darts/pull/924) by [Mike Richman](https://github.com/zgana).
+- 🔴 `HNiTSModel` renamed to `HNiTS` [#1000](https://github.com/unit8co/darts/pull/1000) by [Greg DeVos](https://github.com/gdevos010).
+
+**Fixed**
+- A bug with `tail()` and `head()` [#942](https://github.com/unit8co/darts/pull/942) by [Julien Herzen](https://github.com/hrzn).
+- An issue with arguments being reverted for the `metric` function of gridsearch and backtest [#989](https://github.com/unit8co/darts/pull/989) by [Clara Grotehans](https://github.com/ClaraGrthns).
+- An error checking whether `fit()` has been called in global models [#944](https://github.com/unit8co/darts/pull/944) by [Julien Herzen](https://github.com/hrzn).
+- An error in Gaussian Process filter happening with newer versions of sklearn [#963](https://github.com/unit8co/darts/pull/963) by [Julien Herzen](https://github.com/hrzn).
+
+### For developers of the library:
+
+**Fixed**
+- An issue with LinearLR scheduler in tests. [#928](https://github.com/unit8co/darts/pull/928) by [Dennis Bader](https://github.com/dennisbader).
 
 
 ## [0.19.0](https://github.com/unit8co/darts/tree/0.19.0) (2022-04-13)

README.md

@@ -31,13 +31,14 @@ series, and some of the models offer a rich support for probabilistic forecastin
 
 ##### High Level Introductions
 * [Introductory Blog Post](https://medium.com/unit8-machine-learning-publication/darts-time-series-made-easy-in-python-5ac2947a8878)
-* [Introduction to Darts at PyData Global 2021](https://youtu.be/g6OXDnXEtFA)
+* [Introduction video (25 minutes)](https://youtu.be/g6OXDnXEtFA)
 
 ##### Articles on Selected Topics
 * [Training Models on Multiple Time Series](https://medium.com/unit8-machine-learning-publication/training-forecasting-models-on-multiple-time-series-with-darts-dc4be70b1844)
 * [Using Past and Future Covariates](https://medium.com/unit8-machine-learning-publication/time-series-forecasting-using-past-and-future-external-data-with-darts-1f0539585993)
 * [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)
 
 ## Quick Install
 
@@ -95,57 +96,64 @@ plt.legend()
 
 ## Features
 * **Forecasting Models:** A large collection of forecasting models; from statistical models (such as
-  ARIMA) to deep learning models (such as N-BEATS). See table of models below.
+  ARIMA) to deep learning models (such as N-BEATS). See [table of models below](#forecasting-models).
+* **Multivariate Support:** `TimeSeries` can be multivariate - i.e., contain multiple time-varying
+  dimensions instead of a single scalar value. Many models can consume and produce multivariate series.
+* **Multiple series training:** All machine learning based models (incl. all neural networks) 
+  support being trained on multiple (potentially multivariate) series. This can scale to large datasets.
+* **Probabilistic Support:** `TimeSeries` objects can (optionally) represent stochastic
+  time series; this can for instance be used to get confidence intervals, and many models support different flavours of probabilistic forecasting (such as estimating parametric distributions 
+  or quantiles).
+* **Past and Future Covariates support:** Many models in Darts support past-observed and/or future-known 
+  covariate (external data) time series as inputs for producing forecasts.
+* **Static Covariates support:** In addition to time-dependent data, `TimeSeries` can also contain
+  static data for each dimension, which can be exploited by some models.
+* **Hierarchical Reconciliation:** Darts offers transformers to perform reconciliation.
+  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.
 * **Data processing:** Tools to easily apply (and revert) common transformations on
-  time series data (scaling, boxcox, ...)
+  time series data (scaling, filling missing values, boxcox, ...)
 * **Metrics:** A variety of metrics for evaluating time series' goodness of fit;
   from R2-scores to Mean Absolute Scaled Error.
 * **Backtesting:** Utilities for simulating historical forecasts, using moving time windows.
-* **Regression Models:** Possibility to predict a time series from lagged versions of itself
-  and of some external covariate series, using arbitrary regression models (e.g. scikit-learn models).
-* **Multiple series training:** All machine learning based models (incl.\ all neural networks) 
-  support being trained on multiple series.
-* **Past and Future Covariates support:** Some models support past-observed and/or future-known covariate time series
-  as inputs for producing forecasts.
-* **Multivariate Support:** Tools to create, manipulate and forecast multivariate time series.
-* **Probabilistic Support:** `TimeSeries` objects can (optionally) represent stochastic
-  time series; this can for instance be used to get confidence intervals, and several models
-  support different flavours of probabilistic forecasting.
 * **PyTorch Lightning Support:** All deep learning models are implemented using PyTorch Lightning,
   supporting among other things custom callbacks, GPUs/TPUs training and custom trainers.
 * **Filtering Models:** Darts offers three filtering models: `KalmanFilter`, `GaussianProcessFilter`,
   and `MovingAverage`, which allow to filter time series, and in some cases obtain probabilistic
   inferences of the underlying states/values.
+* **Datasets** The `darts.datasets` submodule contains some popular time series datasets for rapid
+  experimentation.
 
 ## Forecasting Models
 Here's a breakdown of the forecasting models currently implemented in Darts. We are constantly working
 on bringing more models and features.
 
-Model | Univariate | Multivariate | Probabilistic | Multiple-series training | Past-observed covariates support | Future-known covariates support | Reference
---- | --- | --- | --- | --- | --- | --- | ---
-`ARIMA` | ✅ | | ✅ | | | ✅ |
-`VARIMA` | ✅ | ✅ | | | | ✅ |
-`AutoARIMA` | ✅ | | | | | ✅ |
-`StatsForecastAutoARIMA` (faster AutoARIMA) | ✅ | | ✅ | | | ✅ | [statsforecast](https://github.com/Nixtla/statsforecast)
-`ExponentialSmoothing` | ✅ | | ✅ | | | |
-`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` | ✅ | | ✅ | | | ✅ | [Prophet repo](https://github.com/facebook/prophet)
-`FFT` (Fast Fourier Transform) | ✅ | | | | | |
-`KalmanForecaster` using the Kalman filter and N4SID for system identification | ✅ | ✅ | ✅ | | | ✅ | [N4SID paper](https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf)
-`Croston` method | ✅ | | | | | |
-`RegressionModel`; generic wrapper around any sklearn regression model | ✅ | ✅ | | ✅ | ✅ | ✅ |
-`RandomForest` | ✅ | ✅ | | ✅ | ✅ | ✅ |
-`LinearRegressionModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-`LightGBMModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-`RNNModel` (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | ✅ | ✅ | ✅ | ✅ | | ✅ | [DeepAR paper](https://arxiv.org/abs/1704.04110)
-`BlockRNNModel` (incl. LSTM and GRU) | ✅ | ✅ | ✅ | ✅ | ✅ | |
-`NBEATSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | [N-BEATS paper](https://arxiv.org/abs/1905.10437)
-`NHiTSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | [N-HiTS paper](https://arxiv.org/abs/2201.12886)
-`TCNModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | [TCN paper](https://arxiv.org/abs/1803.01271), [DeepTCN paper](https://arxiv.org/abs/1906.04397), [blog post](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4)
-`TransformerModel` | ✅ | ✅ | ✅ | ✅ | ✅ | |
-`TFTModel` (Temporal Fusion Transformer) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html)
-Naive Baselines | ✅ | | | | | |
+Model | Univariate | Multivariate | Probabilistic | Multiple-series training | Past-observed covariates support | Future-known covariates | Static covariates support | Reference
+--- | --- | --- | --- | --- | --- | --- | --- | ---
+`ARIMA` | ✅ | | ✅ | | | ✅ | |
+`VARIMA` | ✅ | ✅ | | | | ✅ | |
+`AutoARIMA` | ✅ | | | | | ✅ | |
+`StatsForecastAutoARIMA` (faster AutoARIMA) | ✅ | | ✅ | | | ✅ | | [statsforecast](https://github.com/Nixtla/statsforecast)
+`ExponentialSmoothing` | ✅ | | ✅ | | | | |
+`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)
+`FFT` (Fast Fourier Transform) | ✅ | | | | | | |
+`KalmanForecaster` using the Kalman filter and N4SID for system identification | ✅ | ✅ | ✅ | | | ✅ | | [N4SID paper](https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf)
+`Croston` method | ✅ | | | | | | |
+`RegressionModel`; generic wrapper around any sklearn regression model | ✅ | ✅ | | ✅ | ✅ | ✅ | |
+`RandomForest` | ✅ | ✅ | | ✅ | ✅ | ✅ | |
+`LinearRegressionModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
+`LightGBMModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
+`RNNModel` (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | ✅ | ✅ | ✅ | ✅ | | ✅ | | [DeepAR paper](https://arxiv.org/abs/1704.04110)
+`BlockRNNModel` (incl. LSTM and GRU) | ✅ | ✅ | ✅ | ✅ | ✅ | | |
+`NBEATSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | | [N-BEATS paper](https://arxiv.org/abs/1905.10437)
+`NHiTSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | | [N-HiTS paper](https://arxiv.org/abs/2201.12886)
+`TCNModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | | [TCN paper](https://arxiv.org/abs/1803.01271), [DeepTCN paper](https://arxiv.org/abs/1906.04397), [blog post](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4)
+`TransformerModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | |
+`TFTModel` (Temporal Fusion Transformer) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html)
+Naive Baselines | ✅ | | | | | | |
 
 
 ## Community & Contact

setup_u8darts.py

@@ -29,7 +29,7 @@ def read_requirements(path):
 
 setup(
     name="u8darts",
-    version="0.19.0",
+    version="0.20.0",
     description="A python library for easy manipulation and forecasting of time series.",
     long_description=LONG_DESCRIPTION,
     long_description_content_type="text/markdown",