updated changelog (#378)

* updated changelog

* updated changelog according to PR comments

* updated setup file

Co-authored-by: pennfranc <[email protected]>

CHANGELOG.md

@@ -4,8 +4,36 @@
 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/develop)
-[Full Changelog](https://github.com/unit8co/darts/compare/0.8.1...develop)
+[Full Changelog](https://github.com/unit8co/darts/compare/0.9.0...develop)
 
+## [0.9.0](https://github.com/unit8co/darts/tree/0.9.0) (2021-07-09)
+### For users of the library:
+
+**Added:**
+- Multiple forecasting models can now produce probabilistic forecasts by specifying a `num_samples` parameter when calling `predict()`. Stochastic forecasts are stored by utilizing the new `samples` dimension in the refactored `TimeSeries` class (see 'Changed' section). Models supporting probabilistic predictions so far are `ARIMA`, `ExponentialSmoothing`, `RNNModel` and `TCNModel`.
+- Introduced `LikelihoodModel` class which is used by probabilistic `TorchForecastingModel` classes in order to make predictions in the form of parametrized distributions of different types.
+- Added new abstract class `TorchParametricProbabilisticForecastingModel` to serve as parent class for probabilistic models.
+- Introduced new `FilteringModel` abstract class alongside `MovingAverage`, `KalmanFilter` and `GaussianProcessFilter` as concrete implementations.
+- Future covariates are now utilized by `TorchForecastingModels` when the forecasting horizon exceeds the `output_chunk_length` of the model. Before, `TorchForecastingModel` instances could only predict beyond their `output_chunk_length` if they were not trained on covariates, i.e. if they predicted all the data they need as input. This restriction has now been lifted by letting a model not only consume its own output when producing long predictions, but also utilizing the covariates known in the future, if available.
+- Added a new `RNNModel` class which utilizes and rnn module as both encoder and decoder. This new class natively supports the use of the most recent future covariates when making a forecast. See documentation for more details.
+- Introduced optional `epochs` parameter to the `TorchForecastingModel.predict()` method which, if provided, overrides the `n_epochs` attribute in that particular model instance and training session.
+- Added support for `TimeSeries` with a `pandas.RangeIndex` instead of just allowing `pandas.DatetimeIndex`.
+- `ForecastingModel.gridsearch` now makes use of parallel computation.
+- Introduced a new `force_reset` parameter to `TorchForecastingModel.__init__()` which, if left to False, will prevent the user from overriding model data with the same name and directory.
+
+
+**Fixed:**
+- Solved bug occurring when training `NBEATSModel` on a GPU.
+- Fixed crash when running `NBEATSModel` with `log_tensorboard=True`
+- Solved bug occurring when training a `TorchForecastingModel` instance with a `batch_size` bigger than the available number of training samples.
+- Some fixes in the documentation, including adding more details
+- Other minor bug fixes
+
+**Changed:**
+- 🔴 The `TimeSeries` class has been refactored to support stochastic time series representation by adding an additional dimension to a time series, namely `samples`. A time series is now based on a 3-dimensional `xarray.DataArray` with shape `(n_timesteps, n_components, n_samples)`. This overhaul also includes a change of the constructor which is incompatible with the old one. However, factory methods have been added to create a `TimeSeries` instance from a variety of data types, including `pd.DataFrame`. Please refer to the documentation of `TimeSeries` for more information.
+- 🔴 The old version of `RNNModel` has been renamed to `BlockRNNModel`.
+- The `historical_forecast()` and `backtest()` methods of `ForecastingModel` have been reorganized a bit by making use of new wrapper methods to fit and predict models.
+- Updated `README.md` to reflect the new additions to the library.
 
 ## [0.8.1](https://github.com/unit8co/darts/tree/0.8.1) (2021-05-22)
 **Fixed:**

setup_u8darts.py

@@ -29,7 +29,7 @@ def read_requirements(path):
 
 setup(
       name='u8darts',
-      version="0.8.1",
+      version="0.9.0",
       description='A python library for easy manipulation and forecasting of time series.',
       long_description=LONG_DESCRIPTION,
       long_description_content_type="text/markdown",