Multivariate times series classification through an interpretable representation
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MultivariateTime series featuresComplexity measuresTime series interpretationClassification
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]
SponsorshipSpanish 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-2017-080137
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.