YfromX

Simple reduction predicting endogeneous from concurrent exogenous variables.

Quickstart

from sktime.forecasting.compose import YfromX

estimator = YfromX(estimator, pooling='local')

Parameters(2)

estimatorsklearn regressor or skpro probabilistic regressor,

must be compatible with sklearn or skpro interface tabular regression algorithm used in reduction algorithm if skpro regressor, resulting forecaster will have probabilistic capability

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

Examples

>>> from sktime.datasets import load_longley
>>> from sktime.split import temporal_train_test_split
>>> from sktime.forecasting.compose import YfromX
>>> from sklearn.linear_model import LinearRegression
>>> 
>>> y, X = load_longley ()
>>> y_train, y_test, X_train, X_test = temporal_train_test_split (y, X)
>>> fh = y_test. index
>>> 
>>> f = YfromX (LinearRegression ())
>>> f. fit (y = y_train, X = X_train, fh = fh) YfromX(
... )
>>> y_pred = f. predict (X = X_test) YfromX can also be used with skpro probabilistic regressors, in this case the resulting forecaster will be capable of probabilistic forecasts:
>>> from skpro.regression.residual import ResidualDouble # doctest: +SKIP
>>> reg_proba = ResidualDouble(LinearRegression()) # doctest: +SKIP
>>> f = YfromX(reg_proba) # doctest: +SKIP
>>> f.fit(y=y_train, X=X_train, fh=fh) # doctest: +SKIP YfromX(…)
>>> y_pred = f.predict_interval(X=X_test) # doctest: +SKIP