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

arXiv: https://arxiv.org/abs/2005.03645 · HTML: https://mlsanity.com/paper/2005.03645/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 6 of 6)

- [An Efficient Federated Distillation Learning System for Multi-task Time Series Classification](https://mlsanity.com/paper/2201.00011/) (2021)
- [Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments](https://mlsanity.com/paper/2304.07250/) (2023)
- [Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies](https://mlsanity.com/paper/2503.23775/) (2025)
- [The Semantic Adjacency Criterion in Time Intervals Mining](https://mlsanity.com/paper/2101.03842/) (2021)
- [Explainable classification of astronomical uncertain time series](https://mlsanity.com/paper/2210.00869/) (2022)
- [LCE: An Augmented Combination of Bagging and Boosting in Python](https://mlsanity.com/paper/2308.07250/) (2023)

## Abstract (arXiv)

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).