Algorithm

Machine learning

Accounting

Data mining

Series (stratigraphy)

Business

Time series

Paleontology

Pattern recognition (psychology)

Artificial intelligence

Multivariate statistics

Variance (accounting)

Computer science

Ensemble learning

Boosting (machine learning)

Divide and conquer algorithms

Biology

Missing data

Classifier (UML)

Data Mining and Knowledge Discovery

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).

XEM: An explainable-by-design ensemble method for multivariate time series classification