Multivariate times series classification through an interpretable representation
Metadatos
Afficher la notice complèteEditorial
Elsevier
Materia
Multivariate Time series features Complexity measures Time series interpretation Classification
Date
2021-05-18Referencia bibliográfica
Francisco J. Baldán, José M. Benítez, Multivariate times series classification through an interpretable representation, Information Sciences, Volume 569, 2021, Pages 596-614, ISSN 0020-0255, [https://doi.org/10.1016/j.ins.2021.05.024]
Patrocinador
Spanish Ministry of Economy and Competitiveness TIN2016-81113-R; Andalusian Regional Government, Spain P12-TIC-2958; FPI from the Spanish Ministry of Economy and Competitiveness BES-2017-080137Résumé
Multivariate time series classification is a machine learning task with increasing importance
due to the proliferation of information sources in different domains (economy,
health, energy, crops, etc.). Univariate methods lack the ability to capture the relationships
between the different variables that compose a multivariate time series and therefore cannot
be directly extrapolated to multivariate environments. Despite the good performance
and competitive results of the multivariate proposals published to date, they are hard to
interpret due to their high complexity. In this paper, we propose a multivariate time series
classification method based on an alternative representation of the time series, composed
of a set of 41 descriptive time series features, in order to improve the interpretability of
time series and results obtained. Our proposal uses traditional classifiers over the extracted
features to look for relationships between the different variables that form a multivariate
time series. We have selected four state-of-the-art algorithms as base classifiers to evaluate
our method. We have tested our proposal on the complete University of East Anglia repository,
obtaining highly interpretable results capable of explaining the relationships
between the features that compose the time series and achieving performance results statistically
indistinguishable from the best algorithms of the state-of-the-art.