  The global diabetes epidemic highlights the importance of maintaining good
glycemic control. Glucose prediction is a fundamental aspect of diabetes
management, facilitating real-time decision-making. Recent research has
introduced models focusing on long-term glucose trend prediction, which are
unsuitable for real-time decision-making and result in delayed responses.
Conversely, models designed to respond to immediate glucose level changes
cannot analyze glucose variability comprehensively. Moreover, contemporary
research generally integrates various physiological parameters (e.g. insulin
doses, food intake, etc.), which inevitably raises data privacy concerns. To
bridge such a research gap, we propose TimeGlu -- an end-to-end pipeline for
short-term glucose prediction solely based on CGM time series data. We
implement four baseline methods to conduct a comprehensive comparative analysis
of the model's performance. Through extensive experiments on two contrasting
datasets (CGM Glucose and Colas dataset), TimeGlu achieves state-of-the-art
performance without the need for additional personal data from patients,
providing effective guidance for real-world diabetic glucose management.
