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 atthis https URL
View on arXiv@article{meng2025_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 }
}