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Although much work has been done on explainability in the computer vision andnatural language processing (NLP) fields, there is still much work to be doneto explain methods applied to time series as time series by nature can not beunderstood at first sight. In this paper, we present a Deep Neural Network(DNN) in a teacher-student architecture (distillation model) that offersinterpretability in time-series classification tasks. The explainability of ourapproach is based on transforming the time series to 2D plots and applyingimage highlight methods (such as LIME and GradCam), making the predictionsinterpretable. At the same time, the proposed approach offers increasedaccuracy competing with the baseline model with the trade-off of increasing thetraining time.

XAI for time-series classification leveraging image highlight methods