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Forecaster

RecursiveReductionForecaster

API referenceSource
Categorical in XInsamplePred int insampleExogenous

Recursive reduction forecaster, incl exogenous Rec.

Implements recursive reduction, of forecasting to tabular regression.

Algorithm details:

In fit, given endogeneous time series y and possibly exogenous X:

fits estimator to feature-label pairs as defined as follows.

features = y(t), y(t-1), …, y(t-window_size), if provided: X(t+1) labels = y(t+1) ranging over all t where the above have been observed (are in the index)

In predict, given possibly exogenous X, at cutoff time c,

applies fitted estimators’ predict to feature = y(c), y(c-1), …, y(c-window_size), if provided: X(c+1) to obtain a prediction for y(c+1). If a given y(t) has not been observed, it is replaced by a prediction obtained in the same way - done repeatedly until all predictions are obtained. Out-of-sample, this results in the “recursive” behaviour, where predictions at time points c+1, c+2, etc, are obtained iteratively. In-sample, predictions are obtained in a single step, with potential missing values obtained via the impute strategy chosen.

Quickstart

python
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