Implet: A Post-hoc Subsequence Explainer for Time Series Models
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
Cite
@article{arxiv.2505.08748,
title = {Implet: A Post-hoc Subsequence Explainer for Time Series Models},
author = {Fanyu Meng and Ziwen Kan and Shahbaz Rezaei and Zhaodan Kong and Xin Chen and Xin Liu},
journal= {arXiv preprint arXiv:2505.08748},
year = {2025}
}