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Multivariate time series classification is a task with increasing importancedue to the proliferation of new problems in various fields (economy, health,energy, transport, crops, etc.) where a large number of information sources areavailable. Direct extrapolation of methods that traditionally worked inunivariate environments cannot frequently be applied to obtain the best resultsin multivariate problems. This is mainly due to the inability of these methodsto capture the relationships between the different variables that conform amultivariate time series. The multivariate proposals published to date offercompetitive results but are hard to interpret. In this paper we propose a timeseries classification method that considers an alternative representation oftime series through a set of descriptive features taking into account therelationships between the different variables of a multivariate time series. Wehave applied traditional classification algorithms obtaining interpretable andcompetitive results.

Multivariable times series classification through an interpretable representation