RecursiveReductionForecaster

Recursive reduction forecaster, incl exogenous Rec.

Quickstart

from sktime.forecasting.compose import RecursiveReductionForecaster

estimator = RecursiveReductionForecaster(estimator, window_length=10, impute_method='bfill', pooling='local')

Parameters(4)

estimatorsklearn regressor, must be compatible with sklearn interface

tabular regression algorithm used in reduction algorithm

window_lengthint, optional, default=10

window length used in the reduction algorithm

impute_methodstr, None, or sktime transformation, optional

Imputation method to use for missing values in the lagged data

• default=”bfill”

• if str, admissible strings are of Imputer.method parameter, see there. To pass further parameters, pass the Imputer transformer directly, as described below.

• if sktime transformer, this transformer is applied to the lagged data. This needs to be a transformer that removes missing data, and can be an Imputer.

• if None, no imputation is done when applying Lag transformer

poolingstr, one of [“local”, “global”, “panel”], optional, default=”local”

level on which data are pooled to fit the supervised regression model “local” = unit/instance level, one reduced model per lowest hierarchy level “global” = top level, one reduced model overall, on pooled data ignoring levels “panel” = second lowest level, one reduced model per panel level (-2) if there are 2 or less levels, “global” and “panel” result in the same if there is only 1 level (single time series), all three settings agree