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TimeSeriesForestRegressor

API referenceSource

Time series forest regressor.

Quickstart

python
from sktime.regression.interval_based import TimeSeriesForestRegressor

estimator = TimeSeriesForestRegressor(min_interval=3, n_estimators=200, n_jobs=1, random_state=None)

Parameters(4)

n_estimatorsint, default=200
Number of estimators.
min_intervalint, default=3
Minimum width 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.

random_stateint, default=None

Examples

>>> from sktime.regression.interval_based import TimeSeriesForestRegressor
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train")
>>> X_test, y_test = load_unit_test (split = "test")
>>> regressor = TimeSeriesForestRegressor (n_estimators = 150)
>>> regressor. fit (X_train, y_train) TimeSeriesForestRegressor(n_estimators=150)
>>> y_pred = regressor. 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 [2 ] Java implementation https://github.com/uea-machine-learning/tsml [3 ] Arxiv paper: https://arxiv.org/abs/1302.2277