SupervisedTimeSeriesForest¶
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class
sktime.classification.interval_based.
SupervisedTimeSeriesForest
(n_estimators=500, n_jobs=1, random_state=None)[source]¶ Supervised Time Series Forest (STSF) classifier as described in [1].
A time series forest is an ensemble of decision trees built on intervals selected through a supervised process.
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
n_estimators : int, ensemble size, optional (default = 200) n_jobs : int, optional (default=1) The number of jobs to run in parallel for both fit and predict.
-1
means using all processors. random_state : int, seed for random, optional (default = none)n_classes : int, extracted from the data classifiers : array of shape = [n_estimators] of DecisionTree classifiers intervals : array of shape = [n_estimators][3][7][n_intervals][2] stores indexes of all start and end points for all classifiers for each representaion and feature
..[1] Cabello, Nestor, et al. “Fast and Accurate Time Series Classification Through Supervised Interval Search.” IEEE ICDM 2020
Java implementation https://github.com/uea-machine-learning/tsml/blob/master/src/main/ java/tsml/classifiers/interval_based/STSF.java