XEM: An Explainable Ensemble Method for Multivariate Time Series Classification.

We present XEM, an eXplainable Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. Our evaluation shows that XEM outperforms the state-of-the-art MTS classifiers on the UEA datasets. Furthermore, XEM provides faithful explainability by design and manifests robust performance when faced with challenges arising from continuous data collection (different MTS length, missing data and noise).