Time series forecasting is streamlined with skforecast, a Python library designed for machine learning models. It targets researchers and practitioners by offering a flexible framework that integrates seamlessly with any scikit-learn compatible estimator. The library provides tools for feature engineering, model selection, hyperparameter tuning, and production-ready validation, enabling efficient development from prototypes to deployed solutions.

How It Works

The core of skforecast lies in its adherence to the scikit-learn API, allowing users to leverage familiar ML algorithms like LightGBM, XGBoost, and CatBoost. It supports both recursive and direct forecasting strategies, catering to single and multi-series scenarios. Key features include automated lag and window feature engineering, robust backtesting for realistic performance evaluation, and hyperparameter tuning capabilities, all designed to produce interpretable and production-ready models.

Quick Start & Requirements

• Installation: pip install skforecast
• Prerequisites: Core dependencies are included. For advanced options, consult the Installation Guide.
• Resources:
  • Documentation: https://skforecast.org
  • Examples & Tutorials: https://skforecast.org/latest/examples/
  • Skforecast Studio (No-code GUI): https://skforecast.org/latest/studio/

Highlighted Details

• Universal scikit-learn estimator compatibility (LightGBM, XGBoost, CatBoost, Keras, etc.).
• Supports single/multi-series, recursive/direct strategies, and probabilistic forecasting.
• Integrated feature engineering (lags, window features) and validation tools.
• AI-assisted forecasting context files for LLM code generation.
• Skforecast Studio offers a visual, no-code interface generating production-ready Python code.

Maintenance & Community

Contributions are welcomed via GitHub Issues for bug reports and feature requests. The project encourages code contributions, example additions, testing, and documentation improvements. Community engagement is fostered through GitHub, with LinkedIn mentioned for broader outreach. Specific links to Discord/Slack or a roadmap are not provided in the README.

Licensing & Compatibility

• Software License: BSD-3-Clause (permissive, suitable for commercial use).
• Documentation License: CC BY-NC-SA 4.0 (requires attribution, non-commercial use, share-alike).
• Trademark: Registered with EUIPO (application number 019109684).

Limitations & Caveats

No specific limitations, alpha status, or known bugs are detailed in the provided README. The project appears stable and well-supported.