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TSPFN addresses the critical challenge of generalizing machine learning models in low-data medical regimes by adapting the TabPFN architecture specifically for physiological time series. By integrating structured temporal representations and extensive pretraining on 140,000 real-world signals, this foundation model achieves superior cross-domain performance compared to both standard tabular baselines and specialized deep learning approaches. The work demonstrates a significant architectural shift required to handle spatio-temporal dependencies inherent in medical monitoring data without relying on conventional fine-tuning.
TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classification
arXiv:2608.31013v1 Announce Type: new Abstract: 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
Abstract: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 this https URL
Accepted at STACOM 2026 (MICCAI Workshop). 10 pages, 3 figures
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2608.31013 [cs.LG]
(or arXiv:2608.31013v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.31013 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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