skforecast/skforecast

Health v2 · maintenance only

Adoption not part of the score

1527 stars · 198 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Skforecast is a Python library for time series forecasting that turns any scikit-learn compatible estimator (LightGBM, XGBoost, CatBoost, Keras, etc.) into a forecaster, alongside statistical methods like ARIMA/SARIMAX and foundation models. It provides multi-series and multi-step forecasting, exogenous variable support, backtesting, and probabilistic forecasting with prediction intervals.

Use cases

• forecast future values of a time series with machine learning models
• predict sales or demand across multiple related time series
• backtest forecasting models with walk-forward validation
• estimate prediction intervals for probabilistic forecasts
• include exogenous variables like weather or holidays in forecasts
• forecast multiple steps ahead with recursive or direct strategies
• compare statistical models like SARIMAX against gradient boosting forecasters

When to choose

• you want scikit-learn compatible regressors for time series forecasting
• you need backtesting, hyperparameter tuning, and prediction intervals in one library
• you forecast many related series with exogenous predictors
• you want a well-documented, actively maintained Python forecasting library

When to avoid

• you need deep learning sequence models like N-BEATS or TFT as first-class citizens
• you want automated forecasting with zero configuration like Prophet or Nixtla AutoML
• your project is not in Python

Facets

library · maturity active

machine-learning data-science benchmarking machine-learning data-science time-series python forecasting time-series-forecasting scikit-learn backtesting probabilistic-forecasting exogenous-variables multi-step-forecasting arima sarimax lightgbm xgboost catboost prediction-intervals

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