UniShape模型通过捕捉关键时间片段提升分类准确率与可解释性。
A Unified Shape-Aware Foundation Model for Time Series Classification
- 引入形状感知适配器，动态选择不同尺度的判别性子序列
- 在189万样本数据上预训练，128个UCR数据集上达顶尖性能
- 适合需要可解释性的时序分类任务，如医疗、工业监控
基于大规模源数据预训练的基础模型正在重塑时序分类的传统训练范式。然而，现有时序基础模型主要关注预测任务，常忽视分类特有的挑战，如建模能捕捉类别判别性时序特征的可解释形状。为弥合这一差距，我们提出UniShape——一个专为时序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，自适应地将多尺度判别性子序列（形状）聚合为类别标记，有效选择最相关的时间尺度以提升模型可解释性。同时，采用原型驱动的预训练模块，联合学习实例级与形状级表示，实现可迁移形状模式的捕捉。在包含189万样本的大规模多领域时序数据集上预训练后，UniShape在多个目标域展现出卓越泛化能力。在128个UCR数据集和30个额外时序数据集上的实验表明，UniShape达到当前最优分类性能，可解释性分析与消融实验进一步验证其有效性。
原文摘要 · Abstract (English)
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.
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