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Time series data is prevalent in a wide variety of real-world applicationsand it calls for trustworthy and explainable models for people to understandand fully trust decisions made by AI solutions. We consider the problem ofbuilding explainable classifiers from multi-variate time series data. A keycriterion to understand such predictive models involves elucidating andquantifying the contribution of time varying input variables to theclassification. Hence, we introduce a novel, modular, convolution-based featureextraction and attention mechanism that simultaneously identifies the variablesas well as time intervals which determine the classifier output. We presentresults of extensive experiments with several benchmark data sets that showthat the proposed method outperforms the state-of-the-art baseline methods onmulti-variate time series classification task. The results of our case studiesdemonstrate that the variables and time intervals identified by the proposedmethod make sense relative to available domain knowledge.

Explainable Multivariate Time Series Classification: A Deep Neural  Network Which Learns To Attend To Important Variables As Well As Informative  Time Intervals