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Classifier

MrSQM

API referenceSource

MrSQM = Multiple Representations Sequence Miner.

Quickstart

python
from sktime.classification.shapelet_based import MrSQM

estimator = MrSQM(strat='RS', features_per_rep=500, selection_per_rep=2000, nsax=1, nsfa=0, custom_config=None, random_state=None, sfa_norm=True)

Parameters(8)

stratstr, one of ‘R’,’S’,’SR’, or ‘RS’, default=”RS”
feature selection strategy. By default set to ‘RS’. R and S are single-stage filters while RS and SR are two-stage filters.
features_per_repint, default=500
(maximum) number of features selected per representation.
selection_per_repint, default=2000
(maximum) number of candidate features selected per representation. Only applied in two stages strategies (RS and SR), otherwise ignored.
nsaxint, default=1
number of representations produced by sax transformation.
nsfaint, default=0
number of representations produced by sfa transformation.
custom_configdict, default=None
customized parameters for the symbolic transformation.
random_stateint, default=None.
random seed for the classifier.
sfa_normbool, default=True.
whether to apply time series normalisation (standardisation).

References

  1. [1 ] Thach Le Nguyen and Georgiana Ifrim. “MrSQM: Fast Time Series Classification with Symbolic Representations and Efficient Sequence Mining” arXiv preprint arXiv:2109.01036 (2021). [2 ] Thach Le Nguyen and Georgiana Ifrim. “Fast Time Series Classification with Random Symbolic Subsequences”. AALTD 2022.