Attention-Based Counterfactual Explanation for Multivariate Time Series

Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi

Abstract: In this paper, we propose Attention-based Counterfactual Explanation (AB-CF), a novel model that generates post-hoc counterfactual explanations for multivariate time series classification that narrow the attention to a few important segments. We validated our model using seven real-world time-series datasets from the UEA repository. Our experimental results show the superiority of AB-CF in terms of validity, proximity, sparsity, contiguity, and efficiency compared with other competing state-of-the-art baselines.