Releases: sktime/pytorch-forecasting
Releases · sktime/pytorch-forecasting
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