make_reduction

make_reduction(estimator, strategy='recursive', window_length=10, scitype='infer', transformers=None)

Make forecaster based on reduction to tabular or time-series regression.

During fitting, a sliding-window approach is used to first transform the time series into tabular or panel data, which is then used to fit a tabular or time-series regression estimator. During prediction, the last available data is used as input to the fitted regression estimator to generate forecasts.

Parameters

estimatoran estimator instance

Either a tabular regressor from scikit-learn or a time series regressor from sktime.

strategystr, optional (default=”recursive”)

The strategy to generate forecasts. Must be one of “direct”, “recursive” or “multioutput”.

window_lengthint, optional (default=10)

Window length used in sliding window transformation.

scitypestr, optional (default=”infer”)

Must be one of “infer”, “tabular-regressor” or “time-series-regressor”. If the scitype cannot be inferred, please specify it explicitly. See scitype.

Returns

estimatoran Estimator instance

A reduction forecaster

References

..[1] Bontempi, Gianluca & Ben Taieb, Souhaib & Le Borgne, Yann-Aël. (2013).

Machine Learning Strategies for Time Series Forecasting.