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.
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Acknowledgments
This project has been supported in part by funding from GEO Directorate under NSF awards #2204363, #2240022, and #2301397 and the CISE Directorate under NSF award #2305781.
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Li, P., Bahri, O., Boubrahimi, S.F., Hamdi, S.M. (2023). Attention-Based Counterfactual Explanation for Multivariate Time Series. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2023. Lecture Notes in Computer Science, vol 14148. Springer, Cham. https://doi.org/10.1007/978-3-031-39831-5_26
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DOI: https://doi.org/10.1007/978-3-031-39831-5_26
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