More testing and interpretation features

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

Fix edge case in prediction logging

Fixes

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

Additions

Add favicon to docs page

Make pip installable from master branch

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

Improving tests

• Add tests for MacOS
• Automatic releases
• Coverage reporting

Patch release

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

Minor release

Minor release

Added

• Basic tests for data and model (mostly integration tests)
• Automatic target normalization
• Improved visualization and logging of temporal fusion transformer
• Model bugfixes and performance improvements for temporal fusion transformer

Modified

• Metrics are reduced to calculating loss. Target transformations are done by new target transformer