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The time series classification literature has expanded rapidly over the lastdecade, with many new classification approaches published each year. Theresearch focus has mostly been on improving the accuracy and efficiency ofclassifiers, while their interpretability has been somewhat neglected.Classifier interpretability has become a critical constraint for manyapplication domains and the introduction of the 'right to explanation' GDPR EUlegislation in May 2018 is likely to further emphasize the importance ofexplainable learning algorithms. In this work we analyse the state-of-the-artfor time series classification, and propose new algorithms that aim to maintainthe classifier accuracy and efficiency, but keep interpretability as a keydesign constraint. We present new time series classification algorithms thatadvance the state-of-the-art by implementing the following three key ideas: (1)Multiple resolutions of symbolic approximations: we combine symbolicrepresentations obtained using different parameters; (2) Multiple domainrepresentations: we combine symbolic approximations in time (e.g., SAX) andfrequency (e.g., SFA) domains; (3) Efficient navigation of a hugesymbolic-words space: we adapt a symbolic sequence classifier named SEQL, tomake it work with multiple domain representations (e.g., SAX-SEQL, SFA-SEQL),and use its greedy feature selection strategy to effectively filter the bestfeatures for each representation. We show that a multi-resolution multi-domainlinear classifier, SAX-SFA-SEQL, achieves a similar accuracy to thestate-of-the-art COTE ensemble, and to a recent deep learning method (FCN), butuses a fraction of the time required by either COTE or FCN. We discuss theaccuracy, efficiency and interpretability of our proposed algorithms. Tofurther analyse the interpretability aspect of our classifiers, we present acase study on an ecology benchmark.

Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains