Forecasting with XGBoost, LightGBM and other Gradient Boosting models¶

Gradient boosting models have gained popularity in the machine learning community due to their ability to achieve excellent results in a wide range of use cases, including both regression and classification. Although these models have traditionally been less common in forecasting, recent research has shown that they can be highly effective in this domain. Some of the key advantages of using gradient boosting models for forecasting include:

• The ease with which exogenous variables, in addition to autoregressive variables, can be incorporated into the model.

• The ability to capture non-linear relationships between variables.

• High scalability, which enables the models to handle large volumes of data.

There are several popular implementations of gradient boosting in Python, with four of the most popular being XGBoost, LightGBM, scikit-learn HistGradientBoostingRegressor and CatBoost. All of these libraries follow the scikit-learn API, making them compatible with skforecast.