M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps

• URL: http://arxiv.org/abs/2411.02649v1
• Date: Mon, 04 Nov 2024 22:16:24 GMT
• Title: M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps
• Authors: Peiyu Li, Omar Bahri, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi,
• Abstract summary: We introduce M-CELS, a counterfactual explanation model designed to enhance interpretability in multidimensional time series classification tasks. Results demonstrate the superior performance of M-CELS in terms of validity, proximity, and sparsity.
• Score: 0.9374652839580181
• License:
• Abstract: 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.

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