Foundation models pre-trained on large-scale source datasets are reshaping the traditional training paradigm for time series classification. However, existing time series foundation models primarily focus on forecasting tasks and often overlook classification-specific challenges, such as modeling interpretable shapelets that capture class-discriminative temporal features. To bridge this gap, we propose UniShape, a unified shape-aware foundation model designed for time series classification. UniShape incorporates a shape-aware adapter that adaptively aggregates multiscale discriminative subsequences (shapes) into class tokens, effectively selecting the most relevant subsequence scales to enhance model interpretability. Meanwhile, a prototype-based pretraining module is introduced to jointly learn instance- and shape-level representations, enabling the capture of transferable shape patterns. Pre-trained on a large-scale multi-domain time series dataset comprising 1.89 million samples, UniShape exhibits superior generalization across diverse target domains. Experiments on 128 UCR datasets and 30 additional time series datasets demonstrate that UniShape achieves state-of-the-art classification performance, with interpretability and ablation analyses further validating its effectiveness.

翻译：基于大规模源数据集预训练的基础模型正在重塑时间序列分类的传统训练范式。然而，现有时间序列基础模型主要关注预测任务，往往忽视分类特有的挑战，例如建模可解释的形状片段以捕捉类别区分性时序特征。为弥补这一差距，我们提出UniShape——一个专为时间序列分类设计的统一形状感知基础模型。UniShape引入形状感知适配器，将多尺度判别子序列（形状）自适应聚合为类别标记，有效选择最相关的子序列尺度以增强模型可解释性。同时，模型采用基于原型的预训练模块联合学习实例级与形状级表征，从而捕捉可迁移的形状模式。通过在包含189万个样本的大规模多领域时间序列数据集上进行预训练，UniShape在多样化目标领域展现出卓越的泛化能力。在128个UCR数据集及30个额外时间序列数据集上的实验表明，UniShape实现了最先进的分类性能，可解释性分析与消融实验进一步验证了其有效性。