为时间序列模型生成精准简洁的时段解释，提升可解释性。
Implet: A Post-hoc Subsequence Explainer for Time Series Models
- 通过识别影响预测的关键时间片段实现子序列级解释。
- 在多个标准时间序列分类数据集上验证，显著提升解释效果。
- 支持群体层面解释，让结果更简洁易懂，适合实际应用。
时间序列模型的可解释性对于建立信任、辅助调试和确保真实场景中的可理解性至关重要。本文提出 Implet，一种新型后处理解释器，能够为时间序列模型生成准确且简洁的子序列级解释。该方法识别出对模型预测有显著影响的时间段，从而在传统特征归因方法的基础上提供更深入的可解释性。在此基础上，我们进一步设计了一种基于队列（群体级别）的解释框架，以进一步提升解释的简洁性和可读性。我们在多个标准时间序列分类基准上评估了 Implet，证明其在增强可解释性方面的有效性。代码已开源于 https://github.com/LbzSteven/implet。
原文摘要 · Abstract (English)
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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