arXiv:2508.01687v1 Announce Type: cross Abstract: Explaining machine learning (ML) models for time series (TS) classification is challenging due to inherent difficulty in raw time series interpretation and doubled down by the high dimensionality. We propose a framework that converts numeric feature attributions from post-hoc, instance-wise explainers (e.g., LIME, SHAP) into structured, human-readable rules. These rules define intervals indicating when and where they apply, improving transparency. Our approach performs comparably to native rule-based methods like Anchor while scaling better to long TS and covering more instances. Rule fusion integrates rule sets through methods such as weighted selection and lasso-based refinement to balance coverage, confidence, and simplicity, ensuring all instances receive an unambiguous, metric-optimized rule. It enhances explanations even for a single explainer. We introduce visualization techniques to manage specificity-generalization trade-offs. By aligning with expert-system principles, our framework consolidates conflicting or overlapping explanations - often resulting from the Rashomon effect - into coherent and domain-adaptable insights. Experiments on UCI datasets confirm that the resulting rule-based representations improve interpretability, decision transparency, and practical applicability for TS classification.