Taming the Glucose Spike: Predicting Postprandial Peaks with Transformers and PyTorch

Living with a Continuous Glucose Monitor (CGM) is like having a dashboard for your metabolism. But for many, it’s a dashboard that only tells you when you've already crashed or spiked. What if we could see 30 minutes into the future? In this guide, we’re moving from reactive monitoring to proactive health. We will leverage time-series forecasting, Transformer models, and PyTorch Forecasting to predict postprandial (after-meal) glucose peaks. By treating blood glucose, insulin doses, and carb intake as multi-dimensional time-series data, we can use the self-attention mechanism to capture long-range dependencies that traditional LSTMs often miss. If you are looking for time-series forecasting, Continuous Glucose Monitoring (CGM), Deep Learning for health, or PyTorch Forecasting tutorials, you’re in the right place! The Architecture: From Sensors to Predictions Predicting glucose isn't just about the last three readings; it's about the context of the last four hours. Our pipeline ingests