skforecast / skforecast

What it does

• Machine learning. any scikit-learn compatible regressor, such as LightGBM, XGBoost or CatBoost, with lags, rolling and calendar features. Recursive or direct strategies, for one series or thousands.
• Foundation models. zero-shot forecasting with pre-trained models such as Chronos-2, TimesFM, Moirai-2 or TabPFN-TS, without training them.
• Statistical models. ARIMA, SARIMAX, ETS and ARAR, with automatic model selection.
• Deep learning. recurrent neural networks (RNN, LSTM) built with Keras.
• Backtesting that reproduces how the model will be used: refits, gaps, fold strides, fixed or expanding windows.