Contributions to PyTorch Forecasting are very welcome! You do not have to be an expert in deep learning to contribute. If you find a bug - fix it! If you miss a feature - propose it!
Open issues to discuss your proposed changes before starting pull requests. This ensures that your contribution will be swiftly integrated into the code base.
Mark your PR with
ready for reviewto indicate that you are done with it and request the maintainers to have a look.
To contribute, fork and clone the repository, install depdencies with
poetry install, create a new branch from master such as
feature/my_new_awesome_model, write your code and create the PR on GitHub.
Backward compatible API if possible to prevent breaking code.
Powerful abstractions to enable quick experimentation. At the same time, the abstractions should allow the user to still take full control.
Intuitive default values that do not need changing in most cases.
Focus on forecasting time-related data - specificially timeseries regression and classificiation. Contributions not directly related to this topic might not be merged. We want to keep the library as crisp as possible.
Always add tests and documentation to new features.