TSPFN将表格模型改造为时序生理信号分类，提升低数据场景下的泛化能力。
TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classification
- 重设计TabPFN架构，加入时序与通道依赖建模机制
- 在14万条真实生理时序上预训练，跨领域性能超越同类模型
- 适合医疗时序分析、小样本学习场景的科研与临床应用
在低至中等数据量条件下实现有效泛化，仍是医学机器学习中生理时序分类的核心挑战。尽管像TabPFN这样的表格基础模型通过上下文学习提供了传统微调的替代方案，但它们并未针对生理信号固有的时序依赖进行设计。本文提出TSPFN，一种重新设计以适应时间序列数据的基底模型。TSPFN融合结构化时序表示与位置嵌入，以捕捉样本内的时间和通道依赖关系。为充分发挥其时空设计优势，该模型在涵盖多个医学领域的140,000条真实生理时序数据上进行预训练。这形成一个统一且可泛化的框架，能够学习医学时序数据的独特特征。在多种生理基准测试中，TSPFN持续优于标准表格基线与TabPFN，且在跨域泛化方面优于专门的深度时序模型。所有实验、消融研究及预处理方案均公开于https://github.com/Jeremstym/TSPFN。
原文摘要 · Abstract (English)
Designing models that generalize effectively in low- to medium-data regimes remains a primary challenge in medical machine learning, particularly for physiological time-series classification. While tabular foundation models such as TabPFN offer an attractive alternative to conventional fine-tuning through in-context learning, they are not designed to capture the temporal dependencies inherent to physiological signals. ~In this paper, we introduce TSPFN, a foundation model that redesigns TabPFN's architecture for time series data. TSPFN integrates structured temporal representations and positional embeddings to capture intra-sample temporal and channel dependencies. To fully leverage its spatio-temporal design, the model is pretrained on 140,000 real-world physiological time series across multiple medical domains. This yields a unified, generalizable framework capable of learning the specificities of medical time series. Experiments across diverse physiological benchmarks demonstrate that TSPFN consistently outperforms standard tabular baselines and TabPFN, and achieves superior cross-domain generalization compared to specialized deep time-series models. All our experiments, ablation studies, and pre-processing scheme are publicly available at https://github.com/Jeremstym/TSPFN
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