skforecast
A Python library for time series forecasting using scikit-learn compatible machine learning models.
What is skforecast?
Skforecast is a Python library for time series forecasting using machine learning models. It provides a comprehensive toolkit that works with any estimator compatible with the scikit-learn API, such as LightGBM, XGBoost, and CatBoost. The library solves the problem of building and deploying reliable forecasting models by offering flexible workflows, feature engineering tools, and production-ready validation methods.
Data scientists, machine learning engineers, and developers who need to build and deploy time series forecasting models using Python and scikit-learn compatible algorithms. It's particularly useful for those working on projects requiring scalable and interpretable predictions.
Developers choose Skforecast for its seamless integration with the scikit-learn ecosystem, which allows them to use familiar machine learning models for forecasting without learning a new API. Its comprehensive tooling for model validation, backtesting, and production deployment provides a significant advantage over building custom forecasting pipelines from scratch.
Overview
Time series forecasting with machine learning models
Use Cases
Best For
- Building machine learning models for univariate time series forecasting
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