Back to models
Forecaster

ResidualBoostingForecaster

In-sample predictionsPrediction intervalsIn-sample prediction intervalsExogenous variables

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_forecaster or residual_forecaster

  • adding exogenous capability to a forecaster, by using it as residual_forecaster, and fitting it on the residuals of an exogenous capable base_forecaster

  • adding probabilistic forecasting capability to a forecaster, by using it as base_forecaster, and adding probability forecasts from a probabilistic forecaster used as residual_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

python
from sktime.forecasting.residual_booster import ResidualBoostingForecaster

estimator = ResidualBoostingForecaster(base_forecaster, residual_forecaster)

Tags

Capabilities

  • In-sample predictions: Supported
  • Prediction intervals
  • In-sample prediction intervals: Supported
  • Exogenous variables
  • Categorical features: Not supported
  • Missing values: Not supported
  • Multivariate: Not supported

Properties

Needs forecast horizon in fitrequires-fh-in-fit
No
X and y need the same indexX-y-must-have-same-index
Yes

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.