About skforecast-ai¶
History¶
skforecast-ai comes from the team behind skforecast. Building a good forecasting model takes many decisions (the forecaster, the estimator, the lags and features, how to validate it), and most of them follow rules that experienced practitioners apply every day. skforecast-ai writes those rules down: a deterministic engine profiles the data, makes every decision with a rule you can read and returns the skforecast script that produced the forecast. An optional LLM explains those decisions, turns domain knowledge into features and a deployment scenario into a backtesting strategy, and never makes a decision on its own. The project builds on the mature skforecast ecosystem and grows alongside it.
Governance¶
skforecast-ai is an open-source project maintained by its core development team, with guidance and valuable contributions from the community. We strongly believe in openness, transparency, and collaboration. Everyone is encouraged to participate through issues, discussions, and pull requests on GitHub.
Core Development Team¶
Meet the core developers behind skforecast-ai.
Contributors¶
View the full list of contributors and their contributions to the project.
Thank you for helping us make skforecast-ai better!
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-ai!
Together, we can make time series forecasting accessible to everyone.
Citing skforecast-ai¶
If you use skforecast-ai in a scientific publication, please cite the version you used. Each version has its own DOI and ready-made citations (APA, BibTeX and others) on Zenodo. To cite skforecast-ai in general, use the DOI that always resolves to the latest release:
Amat Rodrigo, J., & Escobar Ortiz, J. skforecast-ai [Computer software]. https://doi.org/10.5281/zenodo.21338159
@software{skforecast-ai,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
title = {skforecast-ai},
license = {Apache-2.0},
url = {https://ai.skforecast.org/},
doi = {10.5281/zenodo.21338159}
}
The citation metadata is in CITATION.cff, which GitHub also offers through its "Cite this repository" button.
License¶
skforecast-ai software: Apache License 2.0
The underlying skforecast engine is distributed under its own BSD-3-Clause License.
skforecast-ai documentation: CC BY-NC-SA 4.0
Trademark: The trademark skforecast is registered with the European Union Intellectual Property Office (EUIPO) under the application number 019109684. Unauthorized use of this trademark, its logo, or any associated visual identity elements is strictly prohibited without the express consent of the owner.