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With the rise in the employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before. Although many different directions regarding interpretability have been explored for visual modalities, time series data has been neglected, with only a handful of methods tested due to their poor intelligibility. We approach the problem of interpretability in a novel way by proposing TSInsight, where we attach an auto-encoder to the classifier with adoi:10.3390/s21217373 pmid:34770678 pmcid:PMC8587116 fatcat:jn6ujhwhqber3g6qab5sr4b4ga