HIVECOTEV2

Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V2.

Quickstart

from sktime.classification.hybrid import HIVECOTEV2

estimator = HIVECOTEV2(stc_params=None, drcif_params=None, arsenal_params=None, tde_params=None, time_limit_in_minutes=0, save_component_probas=False, verbose=0, n_jobs=1, random_state=None)

Parameters(9)

stc_paramsdict or None, default=None

Parameters for the ShapeletTransformClassifier module. If None, uses the default parameters with a 2 hour transform contract.

drcif_paramsdict or None, default=None

Parameters for the DrCIF module. If None, uses the default parameters with n_estimators set to 500.

arsenal_paramsdict or None, default=None

Parameters for the Arsenal module. If None, uses the default parameters.

tde_paramsdict or None, default=None

Parameters for the TemporalDictionaryEnsemble module. If None, uses the default parameters.

time_limit_in_minutesint, default=0

Time contract to limit build time in minutes, overriding n_estimators/n_parameter_samples for each component. Default of 0 means n_estimators/n_parameter_samples for each component is used.

save_component_probasbool, default=False

When predict/predict_proba is called, save each HIVE-COTEV2 component probability predictions in component_probas.

verboseint, default=0

Level of output printed to the console (for information only).

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.

References

1. [1 ] Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony Bagnall. “HIVE-COTE 2.0: a new meta ensemble for time series classification.” Machine Learning (2021).