DummyGlobalForecaster

Dummy global forecaster that predicts mean of pretrain data.

Quickstart

from sktime.forecasting.dummy_global import DummyGlobalForecaster

estimator = DummyGlobalForecaster(strategy='mean')

Parameters(1)

strategystr, one of {“mean”, “last”, “mean_by_index”}, default=”mean”

Strategy for prediction:

• "mean": predict mean of all values in pretrain set

• "last": predict last value from fit data

• "mean_by_index": predict mean computed per time index across pretraining series. Useful for cold start scenarios where pattern by index matters.

Examples

>>> from sktime.forecasting.dummy_global import DummyGlobalForecaster
>>> from sktime.utils._testing.hierarchical import _make_hierarchical
>>> # Create panel of training data
>>> y_panel = _make_hierarchical (
... hierarchy_levels = (2,), min_timepoints = 10, max_timepoints = 10
... )
>>> forecaster = DummyGlobalForecaster ()
>>> forecaster. pretrain (y_panel) # Learn global mean DummyGlobalForecaster()
>>> # Now fit to a specific series
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> forecaster. fit (y) # Set context DummyGlobalForecaster()
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ]) # Predict global mean
>>> y_pred. shape (3,)