Over the past decade, multivariate time series classification has received great attention. Machine learning (ML) models for multivariate time series classification have made significant strides and achieved impressive success in a wide range of applications and tasks. The challenge of many state-of-the-art ML models is a lack of transparency and interpretability. In this work, we introduce M-CELS, a counterfactual explanation model designed to enhance interpretability in multidimensional time series classification tasks. Our experimental validation involves comparing M-CELS with leading state-of-the-art baselines, utilizing seven real-world time-series datasets from the UEA repository. The results demonstrate the superior performance of M-CELS in terms of validity, proximity, and sparsity, reinforcing its effectiveness in providing transparent insights into the decisions of machine learning models applied to multivariate time series data.
@article{arxiv.2411.02649,
title = {M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps},
author = {Peiyu Li and Omar Bahri and Soukaina Filali Boubrahimi and Shah Muhammad Hamdi},
journal= {arXiv preprint arXiv:2411.02649},
year = {2024}
}
Comments
Accepted at ICMLA 2024. arXiv admin note: text overlap with arXiv:2410.20539