Traditional models struggle with non-stationary Continuous Glucose Monitoring data due to complex metabolic factors. A new approach uses the Temporal Fusion Transformer (TFT) within PyTorch Forecasting to predict hyperglycemic events. This architecture captures long-range dependencies, like delayed carb impacts, offering a more accurate warning system than simple moving averages or LSTMs.

The system builds a reactive pipeline from sensor to alert. Data flows from a CGM sensor and nutritional logs into InfluxDB, where it's pre-processed and feature-engineered. A TimeSeriesDataSet is fed into the TFT model, which outputs probability distributions. These predictions are visualized in Grafana, with the 90th quantile serving as a critical warning line for potential spikes.

This method leverages Transformer Multi-Head Attention to weigh specific data pulses, overcoming LSTM's vanishing gradient issues. By integrating PyTorch Forecasting with InfluxDB and Grafana, developers can create a robust, production-ready health monitoring system. The tutorial provides a practical blueprint for applying advanced AI to wearable data, emphasizing the importance of context in time-series forecasting for HealthTech.