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arXiv · 2505.08748

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

Fanyu Meng·Ziwen Kan·Shahbaz Rezaei·Zhaodan Kong·Xin Chen·Xin Liu
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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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Fanyu Meng, Ziwen Kan, Shahbaz Rezaei, Zhaodan Kong, Xin Chen, Xin Liu. 2025-05-13. Implet: A Post-hoc Subsequence Explainer for Time Series Models. https://arxiv.org/abs/2505.08748

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