Multivariable times series classification through an interpretable representation

TL;DR

This paper introduces an interpretable method for multivariate time series classification that uses descriptive features to capture variable relationships, achieving competitive results with better interpretability.

Contribution

The paper proposes a novel feature-based representation for multivariate time series that enhances interpretability while maintaining competitive classification performance.

Findings

Achieved competitive classification accuracy.

Provided interpretable features reflecting variable relationships.

Demonstrated effectiveness across various datasets.

Abstract

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