Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting

• URL: http://arxiv.org/abs/2606.19560v1
• Date: Wed, 17 Jun 2026 20:01:48 GMT
• Title: Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting
• Abstract summary: Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year.<n>Reliable forecasts of epidemic time series can inform vaccination timing, hospital staffing, and resource allocation.<n>We evaluate regional influenza forecasting using influenza-like illness surveillance and influenza-associated hospitalization time series.
• Score: 1.7191952770783792
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year, making accurate short-term forecasting a core public-health need. Reliable forecasts of epidemic time series can inform vaccination timing, hospital staffing, and resource allocation, yet the comparative behavior of modern forecasting architectures on infectious-disease surveillance data remains insufficiently characterized. We address this gap through a systematic evaluation of regional influenza forecasting using influenza-like illness surveillance and influenza-associated hospitalization time series under both temporal and spatial generalization settings for 1-4-week-ahead prediction. We compare classical neural network architectures, numerical transformer-based models, pretrained time series foundation models, and LLM-based forecasting approaches. Across tasks, we demonstrate that a mixture-of-experts model that fuses multiple pretrained forecasters achieves the strongest overall performance, indicating that heterogeneous pretrained representations provide complementary predictive information. Our results further show that numerical transformer-based models produce reliable forecasts, while pretraining provides the largest gains at longer horizons, particularly when the pretraining domain is mechanistically aligned with influenza dynamics. In contrast, LLM-based time series methods underperform relative to numerical forecasters in this setting. Finally, we examine hospitalization information as both an auxiliary covariate and a pretraining source. Hospitalization signals provide complementary improvements in selected settings and clarify when additional surveillance streams enhance the robustness of multi-horizon forecasting. These findings provide actionable guidance on model selection, pretraining strategy, and auxiliary-signal use for influenza preparedness.

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