  We present XEM, an eXplainable-by-design 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 public 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).
