Explaining machine learning (ML) models for time series (TS) classification remains challenging due to the difficulty of interpreting raw time series and the high dimensionality of the input space. We introduce PHAR--Post-hoc Attribution Rules--a unified framework that transforms numeric feature attributions from post-hoc, instance-wise explainers (e.g. LIME, SHAP) into structured, human-readable rules. These rules define human-readable intervals that indicate where and when decision-relevant segments occur and can enhance model transparency by localizing threshold-based conditions on the raw series. PHAR performs comparably to native rule-based methods, such as Anchor, while scaling more efficiently to long TS sequences and achieving broader instance coverage. A dedicated rule fusion step consolidates rule sets using strategies like weighted selection and lasso-based refinement, balancing key quality metrics: coverage, confidence, and simplicity. This fusion ensures each instance receives a concise and unambiguous rule, improving both explanation fidelity and consistency. We further introduce visualization techniques to illustrate specificity-generalization trade-offs in the derived rules. PHAR resolves conflicting and overlapping explanations--a common effect of the Rashomon phenomenon--into coherent, domain-adaptable insights. Comprehensive experiments on UCR/UEA Time Series Classification Archive demonstrate that PHAR may improve interpretability, decision transparency, and practical applicability for TS classification tasks by providing concise, human-readable rules aligned with model predictions.

翻译：时间序列（TS）分类的机器学习（ML）模型解释仍面临挑战，这源于原始时间序列的可解释性困难以及输入空间的高维特性。本文提出PHAR——事后归因规则——一个将事后实例级解释器（如LIME、SHAP）产生的数值特征归因转化为结构化、人类可读规则的统一框架。这些规则定义了人类可读的区间，指示决策相关片段出现的位置与时机，并能通过对原始序列定位基于阈值的条件来增强模型透明度。PHAR在性能上可与原生基于规则的方法（如Anchor）相媲美，同时能更高效地扩展到长时序序列，并获得更广泛的实例覆盖。通过加权选择与基于LASSO的优化等策略，专门的规则融合步骤可整合规则集，平衡覆盖度、置信度与简洁性这三个关键质量指标。该融合确保每个实例获得简洁明确的规则，从而提升解释的保真度与一致性。我们进一步引入可视化技术来展示衍生规则中特异性与泛化性的权衡。PHAR能够将相互冲突和重叠的解释——拉什蒙现象（Rashomon phenomenon）的常见效应——转化为连贯且可适应领域知识的洞见。在UCR/UEA时间序列分类档案库上的综合实验表明，通过提供与模型预测一致且简洁可读的规则，PHAR可提升时间序列分类任务的可解释性、决策透明度及实际适用性。