Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting

Bowen Zhang, Hsiu-Wen Cheng, Hongyu Yang, Evie L. Shen, Joleen Vansomphone et al.|September 10, 2026arXiv

Key Takeaway

Foundation models for time-series forecasting need task-specific fine-tuning to work well for glucose prediction, and multimodal dietary data (food images + nutrition) provides clinically useful signals that significantly improve postprandial glucose forecasting.

Summary

This study evaluates whether time-series foundation models can forecast blood glucose levels from continuous glucose monitoring (CGM) data, and whether adding dietary information improves predictions.

evaluation multimodal applications

Key Terms

foundation-models zero-shot-generalization fine-tuning multimodal-input postprandial