Implet: A Post-hoc Subsequence Explainer for Time Series Models

• URL: http://arxiv.org/abs/2505.08748v1
• Date: Tue, 13 May 2025 17:01:23 GMT
• Title: Implet: A Post-hoc Subsequence Explainer for Time Series Models
• Authors: Fanyu Meng, Ziwen Kan, Shahbaz Rezaei, Zhaodan Kong, Xin Chen, Xin Liu,
• Abstract summary: We introduce Implet, a novel post-hoc explainer for time series models.<n>Our approach identifies critical temporal segments that significantly contribute to the model's predictions.<n>We propose a cohort-based (group-level) explanation framework.
• Score: 7.890123610098698
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Explainability in time series models is crucial for fostering trust, facilitating debugging, and ensuring interpretability in real-world applications. In this work, we introduce Implet, a novel post-hoc explainer that generates accurate and concise subsequence-level explanations for time series models. Our approach identifies critical temporal segments that significantly contribute to the model's predictions, providing enhanced interpretability beyond traditional feature-attribution methods. Based on it, we propose a cohort-based (group-level) explanation framework designed to further improve the conciseness and interpretability of our explanations. We evaluate Implet on several standard time-series classification benchmarks, demonstrating its effectiveness in improving interpretability. The code is available at https://github.com/LbzSteven/implet

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