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SupervisedTimeSeriesForest

API referenceSource
Predict proba

Supervised Time Series Forest (STSF).

An ensemble of decision trees built on intervals selected through a supervised process as described in _[1]. Overview: Input n series length m For each tree

  • sample X using class-balanced bagging

  • sample intervals for all 3 representations and 7 features using supervised

  • method

  • find mean, median, std, slope, iqr, min and max using their corresponding

  • interval for each rperesentation, concatenate to form new data set

  • build decision tree on new data set

Ensemble the trees with averaged probability estimates.

Quickstart

python
from sktime.classification.interval_based import SupervisedTimeSeriesForest

estimator = SupervisedTimeSeriesForest(n_estimators=200, n_jobs=1, random_state=None)

Parameters(3)

n_estimatorsint, default=200
Number of estimators to build for the ensemble.
n_jobsint, default=1

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

random_stateint or None, default=None
Seed for random number generation.

Examples

>>> from sktime.classification.interval_based import SupervisedTimeSeriesForest
>>> 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 = SupervisedTimeSeriesForest (n_estimators = 5)
>>> clf. fit (X_train, y_train) SupervisedTimeSeriesForest(n_estimators=5)
>>> y_pred = clf. predict (X_test)

References

[1]

Cabello, Nestor, et al. “Fast and Accurate Time Series Classification Through Supervised Interval Search.” IEEE ICDM 2020