Contribute ========== 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.