Implet: A Post-hoc Subsequence Explainer for Time Series Models
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909609197830144 |
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| author | Meng, Fanyu Kan, Ziwen Rezaei, Shahbaz Kong, Zhaodan Chen, Xin Liu, Xin |
| author_facet | Meng, Fanyu Kan, Ziwen Rezaei, Shahbaz Kong, Zhaodan Chen, Xin Liu, Xin |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08748 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Implet: A Post-hoc Subsequence Explainer for Time Series Models Meng, Fanyu Kan, Ziwen Rezaei, Shahbaz Kong, Zhaodan Chen, Xin Liu, Xin Machine Learning 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 |
| title | Implet: A Post-hoc Subsequence Explainer for Time Series Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.08748 |