Attention-Based Counterfactual Explanation for Multivariate Time Series
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-39831-5_26
Reference10 articles.
1. Bagnall, A., et al.: The UEA multivariate time series classification archive. arXiv preprint arXiv:1811.00075 (2018)
2. Bahri, O., Boubrahimi, S.F., Hamdi, S.M.: Shapelet-based counterfactual explanations for multivariate time series. arXiv preprint arXiv:2208.10462 (2022)
3. Bahri, O., Li, P., Boubrahimi, S.F., Hamdi, S.M.: Temporal rule-based counterfactual explanations for multivariate time series. In: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1244–1249. IEEE (2022)
4. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);E Delaney,2021
5. Filali Boubrahimi, S., Hamdi, S.M.: On the mining of time series data counterfactual explanations using barycenters. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 3943–3947 (2022)
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1. Sub-SpaCE: Subsequence-Based Sparse Counterfactual Explanations for Time Series Classification Problems;Communications in Computer and Information Science;2024
2. CELS: Counterfactual Explanations for Time Series Data via Learned Saliency Maps;2023 IEEE International Conference on Big Data (BigData);2023-12-15
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