# 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/0.4.0...develop)

## [0.4.0](https://github.com/unit8co/darts/tree/0.4.0) (2020-10-28)

[Full Changelog](https://github.com/unit8co/darts/compare/0.3.0...0.4.0)

### For users of the library:
**Added:**
- Data (pre) processing abilities using `DataTransformer`, `Pipeline`:
  - `DataTransformer` provide a unified interface to apply transformations on `TimeSeries`, using their `transform()` method
  - `Pipeline`:
    - allow chaining of `DataTransformers`
    - provide `fit()`, `transform()`, `fit_transform()` and `inverse_transform()` methods.
  - Implementing your own data transformers:
    - Data transformers which need to be fitted first should derive from the `FittableDataTransformer` base class and implement a `fit()` method. Fittable transformers also provide a `fit_transform()` method, which fits the transformer and then transforms the data with a single call.
    - Data transformers which perform an invertible transformation should derive from the `InvertibleDataTransformer` base class and implement a `inverse_transform()` method.
    - Data transformers wich are neither fittable nor invertible should derive from the `BaseDataTransformer` base class
    - All data transformers must implement a `transform()` method.
- Concrete `DataTransformer` implementations:
  - `MissingValuesFiller` wraps around `fill_missing_value()` and allows to fill missing values using either a constant value or the `pd.interpolate()` method.
  - `Mapper` and `InvertibleMapper` allow to easily perform the equivalent of a `map()` function on a TimeSeries, and can be made part of a `Pipeline`
  - `BoxCox` allows to apply a BoxCox transformation to the data
- Extended `map()` on `TimeSeries` to accept functions which use both a value and its timestamp to compute a new value e.g.`f(timestamp, datapoint) = new_datapoint`
- Two new forecasting models:
  - `TransformerModel`, an implementation based on the architecture described in [Attention Is All You Need](https://arxiv.org/abs/1706.03762) by Vaswani et al. (2017)
  - `NBEATSModel`, an implementation based on the N-BEATS architecture described in [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://openreview.net/forum?id=r1ecqn4YwB) by Boris N. Oreshkin et al. (2019)

**Changed:**
- 🔴 Removed `cols` parameter from `map()`. Using indexing on `TimeSeries` is preferred.
  ```python
  # Assuming a multivariate TimeSeries named series with 3 columns or variables.
  # To apply fn to columns with names '0' and '2':

  #old syntax
  series.map(fn, cols=['0', '2']) # returned a time series with 3 columns
  #new syntax
  series[['0', '2']].map(fn) # returns a time series with only 2 columns
  ```
- 🔴 Renamed `ScalerWrapper` into `Scaler`
- 🔴 Renamed the `preprocessing` module into `dataprocessing`
- 🔴 Unified `auto_fillna()` and `fillna()` into a single `fill_missing_value()` function
  ```python
  #old syntax
  fillna(series, fill=0)

  #new syntax
  fill_missing_values(series, fill=0)

  #old syntax
  auto_fillna(series, **interpolate_kwargs)

  #new syntax
  fill_missing_values(series, fill='auto', **interpolate_kwargs)
  fill_missing_values(series, **interpolate_kwargs) # fill='auto' by default
  ```

### For developers of the library
### Changed
- GitHub release workflow is now triggered manually from the GitHub "Actions" tab in the repository, providing a `#major`, `#minor`, or `#patch` argument. [\#211](https://github.com/unit8co/darts/pull/211)
- (A limited number of) notebook examples are now run as part of the GitHub develop workflow.

## [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)