Feature/changelog (#193)

* feat(changelog): Generated a CHANGELOG.md using github_changelog_generator and edited for upcoming version to be more insightful

* fix(changelog): GitHub doesn't display style in .md files. Replaced with red unicode symbol

* fix(readme): Added a line to the Contribute section to encourage keeping the changelog up-to-date

* fix(changelog, readme): Implemented code review suggestions

* fix(changelog): Added 'Full Changelog' link under the 0.3.0 version section

* fix(changelog): Cleaned up older versions, ready for release

* fix(changelog, readme): Review suggestions

* fix(changelog): Removed comment

* fix(changelog): Small addition

* fix(changelog): Date update

* fix(changelog): Date update

Author: LeoTafti

CHANGELOG.md

@@ -0,0 +1,81 @@
+# Changelog
+
+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/0.3.0...develop)
+
+## [0.3.0](https://github.com/unit8co/darts/tree/0.3.0) (2020-10-05)
+
+[Full Changelog](https://github.com/unit8co/darts/compare/0.2.3...0.3.0)
+
+### For users of the library:
+**Added:**
+
+- Better indexing on TimeSeries (support for column/component indexing) [\#150](https://github.com/unit8co/darts/pull/150)
+- New `FourTheta` forecasting model [\#123](https://github.com/unit8co/darts/pull/123), [\#156](https://github.com/unit8co/darts/pull/156)
+- `map()` method for TimeSeries [\#121](https://github.com/unit8co/darts/issues/121), [\#166](https://github.com/unit8co/darts/pull/166)
+- Further improved the backtesting functions [\#111](https://github.com/unit8co/darts/pull/111):
+  - Added support for multivariate TimeSeries and models
+  - Added `retrain` and `stride` parameters
+- Custom style for matplotlib plots [\#191](https://github.com/unit8co/darts/pull/191)
+- sMAPE metric [\#129](https://github.com/unit8co/darts/pull/129)
+- Option to specify a `random_state` at model creation using the `@random_method` decorator on models using neural networks to allow reproducibility of results [\#118](https://github.com/unit8co/darts/pull/118)
+
+**Changed:**
+
+- 🔴 **Refactored backtesting** [\#184](https://github.com/unit8co/darts/pull/184)
+  - Moved backtesting functionalities inside `ForecastingModel` and `RegressionModel`
+    ```python
+    # old syntax:
+    backtest_forecasting(forecasting_model, *args, **kwargs)
+
+    # new syntax:
+    forecasting_model.backtest(*args, **kwargs)
+
+    # old syntax:
+    backtest_regression(regression_model, *args, **kwargs)
+
+    # new syntax:
+    regression_model.backtest(*args, **kwargs)
+    ```
+  - Consequently removed the `backtesting` module
+- 🔴 `ForecastingModel` `fit()` **method syntax** using TimeSeries indexing instead of additional parameters [\#161](https://github.com/unit8co/darts/pull/161)
+  ```python
+  # old syntax:
+  multivariate_model.fit(multivariate_series, target_indices=[0, 1])
+
+  # new syntax:
+  multivariate_model.fit(multivariate_series, multivariate_series[["0", "1"]])
+
+  # old syntax:
+  univariate_model.fit(multivariate_series, component_index=2)
+
+  # new syntax:
+  univariate_model.fit(multivariate_series["2"])
+  ```
+
+**Fixed:**
+- Solved issue of TorchForecastingModel.predict(n) throwing an error at n=1. [\#108](https://github.com/unit8co/darts/pull/108)
+- Fixed MASE metrics [\#129](https://github.com/unit8co/darts/pull/129)
+- \[BUG\] ForecastingModel.backtest: Can bypass sanity checks [\#188](https://github.com/unit8co/darts/issues/188)
+- ForecastingModel.backtest\(\) fails if forecast\_horizon isn't provided [\#186](https://github.com/unit8co/darts/issues/186)
+
+### For developers of the library
+
+**Added:**
+- Gradle to build docs, docker image, run tests, … [\#112](https://github.com/unit8co/darts/pull/112), [\#127](https://github.com/unit8co/darts/pull/127), [\#159](https://github.com/unit8co/darts/pull/159)
+- M4 competition benchmark and notebook to the examples [\#138](https://github.com/unit8co/darts/pull/138)
+- Check of test coverage [\#141](https://github.com/unit8co/darts/pull/141)
+
+**Changed:**
+- Dependencies' versions are now fixed [\#173](https://github.com/unit8co/darts/pull/173)
+- Workflow: tests trigger on Pull Request [\#165](https://github.com/unit8co/darts/pull/165)
+
+**Fixed:**
+- Passed the `freq` parameter to the `TimeSeries` constructor in all TimeSeries generating functions [\#157](https://github.com/unit8co/darts/pull/157)
+
+## Older releases
+
+[Full Changelog](https://github.com/unit8co/darts/compare/f618c4536bf7ed6e3b6a2239fbca4e3089736426...0.2.3)

README.md

@@ -98,7 +98,7 @@ from R2-scores to Mean Absolute Scaled Error.
 **Backtesting:** Utilities for simulating historical forecasts, using moving time windows.
 
 **Regressive Models:** Possibility to predict a time series from several other time series 
-(e.g., external regressors), using arbitrary regressive models.
+(e.g., external regressors), using arbitrary regressive models
 
 **Multivariate Support:** Tools to create, manipulate and forecast multivariate time series.
 
@@ -122,8 +122,10 @@ Before working on a contribution (a new feature or a fix) make sure you can't fi
         * …
     * Work on your update
 6. Check that your code pass the tests / design new unit tests: `python -m unittest`.
-7. Verify your tests coverage with `./coverage.sh` (additionaly you can generate xml report and use VSCode Coverage gutter to identify untested lines with `./coverage.sh xml`).
-8. Create a pull request from your new branch to the **develop** branch.
+7. Verify your tests coverage by running `./gradlew coverageTest`
+    * Additionally you can generate an xml report and use VSCode Coverage gutter to identify untested lines with `./coverage.sh xml`
+8. If your contribution introduces a significant change, add it to `CHANGELOG.md` under the "Unreleased" section.
+9. Create a pull request from your new branch to the **develop** branch.
 
 ## Contact Us
 
@@ -137,7 +139,7 @@ Some of the models depend on `fbprophet` and `torch`, which have non-Python depe
 A Conda environment is thus recommended because it will handle all of those in one go.
 
 The following steps assume running inside a conda environment. 
-If that's not possible, first follow the official instructions to install 
+If that's not possible, first follow the official instructions to install
 [fbprophet](https://facebook.github.io/prophet/docs/installation.html#python)
 and [torch](https://pytorch.org/get-started/locally/), then skip to 
 [Install darts](#install-darts)