Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals

Saurabh Kataria; Yi Wu; Zhaoliang Chen; Hyunjung Gloria Kwak; Yuhao Xu; Lovely Yeswanth Panchumarthi; Ran Xiao; Jiaying Lu; Ayca Ermis; Anni Zhao; Runze Yan; Alex Federov; Zewen Liu; Xu Wu; Wei Jin; Carl Yang; Jocelyn Grunwell; Stephanie R. Brown; Amit Shah; Craig Jabaley; Tim Buchman; Sivasubramanium V Bhavani; Randall J. Lee; Xiao Hu

arXiv · 2510.14254

Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals

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Abstract

Foundation models are large-scale machine learning models that are pre-trained on massive amounts of data and can be adapted for various downstream tasks. They have been extensively applied to tasks in Natural Language Processing and Computer Vision with models such as GPT, BERT, and CLIP. They are now also increasingly gaining attention in time-series analysis, particularly for physiological sensing. However, most time series foundation models are specialist models - with data in pre-training and testing of the same type, such as Electrocardiogram, Electroencephalogram, and Photoplethysmogram (PPG). Recent works, such as MOMENT, train a generalist time series foundation model with data from multiple domains, such as weather, traffic, and electricity. This paper aims to conduct a comprehensive benchmarking study to compare the performance of generalist and specialist models, with a focus on PPG signals. Through an extensive suite of total 51 tasks covering cardiac state assessment, laboratory value estimation, and cross-modal inference, we comprehensively evaluate both models across seven dimensions, including win score, average performance, feature quality, tuning gain, performance variance, transferability, and scalability. These metrics jointly capture not only the models' capability but also their adaptability, robustness, and efficiency under different fine-tuning strategies, providing a holistic understanding of their strengths and limitations for diverse downstream scenarios. In a full-tuning scenario, we demonstrate that the specialist model achieves a 27% higher win score. Finally, we provide further analysis on generalization, fairness, attention visualizations, and the importance of training data choice.

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Saurabh Kataria, Yi Wu, Zhaoliang Chen, Hyunjung Gloria Kwak, Yuhao Xu, Lovely Yeswanth Panchumarthi, Ran Xiao, Jiaying Lu, Ayca Ermis, Anni Zhao, Runze Yan, Alex Federov, Zewen Liu, Xu Wu, Wei Jin, Carl Yang, Jocelyn Grunwell, Stephanie R. Brown, Amit Shah, Craig Jabaley, Tim Buchman, Sivasubramanium V Bhavani, Randall J. Lee, Xiao Hu. 2025-10-16. Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals. https://arxiv.org/abs/2510.14254

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