SOURCE-LINKED INTELLIGENCE
Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting
Continuous glucose monitoring (CGM) provides high-frequency measurements of glucose dynamics and enables short-term glucose forecasting for diabetes management. Although time-series foundation models have shown strong general forecasting ability, their effectiveness for CGM prediction and the added value of multimodal dietary context remain unclear. We conduct a comprehensive empirical study using eight public CGM datasets spanning Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol across multiple context lengths and prediction horizons, zero-shot foundati
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- arXiv · AI, language, vision and robotics · 2026-09-10T17:43:29.000Z
First collected: 2026-09-20T18:42:18.733Z. This is not the publication date.