M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps
This addresses interpretability for users of multivariate time series classification models, though it appears incremental as it builds on existing counterfactual explanation methods.
The authors tackled the lack of transparency in multivariate time series classification models by introducing M-CELS, a counterfactual explanation model that achieved superior performance in validity, proximity, and sparsity compared to state-of-the-art baselines on seven real-world datasets.
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.