ResidualBoostingForecaster
Residual boosting forecast fitting one forecaster on residuals of another.
Residual boosting can be used for:
improving forecasts from one forecaster with another, by using either as
base_forecasterorresidual_forecasteradding exogenous capability to a forecaster, by using it as
residual_forecaster, and fitting it on the residuals of an exogenous capablebase_forecasteradding probabilistic forecasting capability to a forecaster, by using it as
base_forecaster, and adding probability forecasts from a probabilistic forecaster used asresidual_forecaster
In fit: fits base_forecaster to y and X, computes in-sample residuals, and fits residual_forecaster to the residuals and X.
In predict, it predicts with both base_forecaster and residual_forecaster, and returns the sum of the two.
Probabilistic forecasts are obtained by shifting quantiles of the residuals forecast by residual_forecaster by the point forecast of the base_forecaster. This requires residual_forecaster to support probabilistic forecasts, but not base_forecaster.
Quickstart
from sktime.forecasting.residual_booster import ResidualBoostingForecaster
estimator = ResidualBoostingForecaster(base_forecaster, residual_forecaster)Parameters(2)
- base_forecastersktime forecaster
- Point-forecast model that may ignore X.
- residual_forecastersktime forecaster
- Model trained on the base model’s in-sample residuals.