Discovering shapelets -- i.e., discriminative temporal patterns within time series -- has been widely studied to address the inherent complexity of time-series classification (TSC) and to make model decision-making processes more transparent. However, existing methods primarily focus on population-level shapelets optimized across the entire dataset, which leads to two fundamental limitations: (i) population-level patterns often misalign with instance-specific features, resulting in suboptimal performance and potentially misleading interpretations, and (ii) most methods treat shapelets as independent entities, overlooking important temporal dependencies and interactions among multiple patterns. To address these limitations, we propose INSHAPE, an interpretable TSC framework that discovers variable-length, discriminative temporal patterns specific to each time series. INSHAPE identifies these patterns as non-overlapping segments and models their temporal dependencies, thereby providing clear instance-level interpretations while achieving strong predictive performance. Furthermore, INSHAPE bridges local and global interpretability through a bottom-up approach, aggregating instance-level shapelets into prototypical (population-level) shapelets. Extensive experiments on 128 UCR and 30 UEA benchmark datasets show that INSHAPE consistently outperforms state-of-the-art shapelet-based methods while providing more intuitive and interpretable insights.

翻译：发现shapelets——即时间序列中具有判别性的时序模式——已被广泛研究，以应对时间序列分类（TSC）的固有复杂性并提升模型决策过程的透明度。然而，现有方法主要关注在整个数据集上优化得到的群体级shapelets，这导致两个根本性局限：（i）群体级模式常与实例特定特征错配，造成性能欠佳及潜在误导性解释；（ii）多数方法将shapelets视为独立实体，忽略了多模式间重要的时序依赖关系与交互作用。针对这些局限，我们提出INSHAPE——一种可解释的TSC框架，该框架能够为每条时间序列发现可变的随机长度、具有判别性的时序模式。INSHAPE将这些模式识别为非重叠时间片段并建模其时序依赖关系，从而在提供清晰实例级解释的同时实现强预测性能。此外，INSHAPE通过自底向上的方法桥接局部与全局可解释性，将实例级shapelets聚合为原型（群体级）shapelets。在128个UCR和30个UEA基准数据集上的大量实验表明，INSHAPE在持续超越最先进的基于shapelets方法的同时，提供了更直观、更具可解释性的洞察。