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!

Contribution guidelines

  • 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 review to 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.

Design principles

  • 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.