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We present XEM, an eXplainable-by-design Ensemble method for Multivariatetime series classification. XEM relies on a new hybrid ensemble method thatcombines an explicit boosting-bagging approach to handle the bias-variancetrade-off faced by machine learning models and an implicit divide-and-conquerapproach to individualize classifier errors on different parts of the trainingdata. Our evaluation shows that XEM outperforms the state-of-the-art MTSclassifiers on the public UEA datasets. Furthermore, XEM provides faithfulexplainability-by-design and manifests robust performance when faced withchallenges arising from continuous data collection (different MTS length,missing data and noise).

XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification