Pytorch-forecasting

- More tests driving coverage to ~90%
- Performance tweaks for temporal fusion transformer
- Reformatting with sort
- Improve documentation - particularly expand on hyper parameter tuning

Fixed

- Fix PoissonLoss quantiles calculation
- Fix N-Beats visualisations

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Added

- Calculating partial dependency for a variable
- Improved documentation - in particular added FAQ section and improved tutorial
- Data for examples and tutorials can now be downloaded. Cloning the repo is not a requirement anymore
- Added Ranger Optimizer from `pytorch_ranger` package and fixed its warnings (part of preparations for conda package release)
- Use GPU for tests if available as part of preparation for GPU tests in CI

Changes

- **BREAKING**: Fix typo "add_decoder_length" to "add_encoder_length" in TimeSeriesDataSet

Bugfixes

- Fixing plotting predictions vs actuals by slicing variables

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Fixed

Fix bug where predictions were not correctly logged in case of `decoder_length == 1`.

Added

- Add favicon to docs page

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Update build system requirements to be parsed correctly when installing with `pip install git+https://github.com/jdb78/pytorch-forecasting`

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- Add tests for MacOS
- Automatic releases
- Coverage reporting

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This release improves robustness of the code.

- Fixing bug across code, in particularly

- Ensuring that code works on GPUs
- Adding tests for models, dataset and normalisers
- Test using GitHub Actions (tests on GPU are still missing)

- Extend documentation by improving docstrings and adding two tutorials.
- Improving default arguments for TimeSeriesDataSet to avoid surprises

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