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:information_source: About The Project
:books: Documentation
:computer: Installation & Dependencies
:sparkles: What is new in skforecast 0.14?
:crystal_ball: Forecasters
:mortar_board: Examples and tutorials
:handshake: How to contribute
:memo: Citation
:money_with_wings: Donating
:scroll: License

About The Project

Skforecast is a Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.

Why use skforecast?

Skforecast simplifies time series forecasting with machine learning by providing:

:jigsaw: Seamless integration with any scikit-learn compatible regressor (e.g., LightGBM, XGBoost, CatBoost, etc.).
:repeat: Flexible workflows that allow for both single and multi-series forecasting.
:hammer_and_wrench: Comprehensive tools for feature engineering, model selection, hyperparameter tuning, and more.
:building_construction: Production-ready models with interpretability and validation methods for backtesting and realistic performance evaluation.

Whether you're building quick prototypes or deploying models in production, skforecast ensures a fast, reliable, and scalable experience.

Get Involved

We value your input! Here are a few ways you can participate:

Report bugs and suggest new features on our GitHub Issues page.
Contribute to the project by submitting code, adding new features, or improving the documentation.
Share your feedback on LinkedIn to help spread the word about skforecast!

Together, we can make time series forecasting accessible to everyone.

Documentation

For detailed information on how to use and leverage the full potential of skforecast please refer to the comprehensive documentation available at:

https://skforecast.org :books:

Documentation
:book: Introduction to forecasting	Basics of forecasting concepts and methodologies
:rocket: Quick start	Get started quickly with skforecast
:hammer_and_wrench: User guides	Detailed guides on skforecast features and functionalities
:mortar_board: Examples and tutorials	Learn through practical examples and tutorials to master skforecast
:question: FAQ and tips	Find answers and tips about forecasting
:books: API Reference	Comprehensive reference for skforecast functions and classes
:black_nib: Authors	Meet the authors and contributors of skforecast

Installation & Dependencies

To install the basic version of skforecast with core dependencies, run the following:

pip install skforecast

For more installation options, including dependencies and additional features, check out our Installation Guide.

What is new in skforecast 0.14?

All significant changes to this project are documented in the release file.

For updates to the latest stable version, see the release notes here.
For updates on the version in development (unstable), see the development release notes.

Forecasters

A Forecaster object in the skforecast library is a comprehensive container that provides essential functionality and methods for training a forecasting model and generating predictions for future points in time.

The skforecast library offers a variety of forecaster types, each tailored to specific requirements such as single or multiple time series, direct or recursive strategies, or custom predictors. Regardless of the specific forecaster type, all instances share the same API.

Forecaster	Single series	Multiple series	Recursive strategy	Direct strategy	Probabilistic prediction	Time series differentiation	Exogenous features	Window features
ForecasterRecursive	:heavy_check_mark:		:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
ForecasterDirect	:heavy_check_mark:			:heavy_check_mark:	:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:
ForecasterRecursiveMultiSeries		:heavy_check_mark:	:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
ForecasterDirectMultiVariate		:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:
ForecasterRNN		:heavy_check_mark:		:heavy_check_mark:
ForecasterSarimax	:heavy_check_mark:		:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:

Examples and tutorials

Explore our extensive list of examples and tutorials (English and Spanish) to get you started with skforecast. You can find them here.

How to contribute

Primarily, skforecast development consists of adding and creating new Forecasters, new validation strategies, or improving the performance of the current code. However, there are many other ways to contribute:

Submit a bug report or feature request on GitHub Issues.
Contribute a Jupyter notebook to our examples.
Write unit or integration tests for our project.
Answer questions on our issues, Stack Overflow, and elsewhere.
Translate our documentation into another language.
Write a blog post, tweet, or share our project with others.

For more information on how to contribute to skforecast, see our Contribution Guide.

Visit our authors section to meet all the contributors to skforecast.

Citation

If you use skforecast for a scientific publication, we would appreciate citations to the published software.

Zenodo

Amat Rodrigo, Joaquin, & Escobar Ortiz, Javier. (2024). skforecast (v0.13.0). Zenodo. https://doi.org/10.5281/zenodo.8382788

APA:

Amat Rodrigo, J., & Escobar Ortiz, J. (2024). skforecast (Version 0.13.0) [Computer software]. https://doi.org/10.5281/zenodo.8382788

BibTeX:

@software{skforecast,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
title = {skforecast},
version = {0.13.0},
month = {8},
year = {2024},
license = {BSD-3-Clause},
url = {https://skforecast.org/},
doi = {10.5281/zenodo.8382788}
}

View the citation file.

Donating

If you found skforecast useful, you can support us with a donation. Your contribution will help to continue developing and improving this project. Many thanks!

License

BSD-3-Clause License