TimeSeriesForestClassifier

Time series forest classifier.

Quickstart

from sktime.classification.interval_based import TimeSeriesForestClassifier

estimator = TimeSeriesForestClassifier(min_interval=3, n_estimators=200, inner_series_length: int | None=None, n_jobs=1, random_state=None)

Parameters(5)

n_estimatorsint, default=200

Number of estimators to build for the ensemble.

min_intervalint, default=3

Minimum length of an interval.

n_jobsint, default=1

The number of jobs to run in parallel for both fit and predict. -1 means using all processors.

inner_series_length: int, default=None

The maximum length of unique segments within X from which we extract intervals is determined. This helps prevent the extraction of intervals that span across distinct inner series.

random_stateint or None, default=None

Seed for random number generation.

Examples

>>> from sktime.classification.interval_based import TimeSeriesForestClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train", return_X_y = True)
>>> X_test, y_test = load_unit_test (split = "test", return_X_y = True)
>>> clf = TimeSeriesForestClassifier (n_estimators = 5)
>>> clf. fit (X_train, y_train) TimeSeriesForestClassifier(n_estimators=5)
>>> y_pred = clf. predict (X_test)

References

1. [1 ] H.Deng, G.Runger, E.Tuv and M.Vladimir, “A time series forest for classification and feature extraction”,Information Sciences, 239, 2013