Double-Diffusion: Balancing Speed, Accuracy, and Uncertainty in Probabilistic Forecasting for Urban Sensor Networks
Double-Diffusion balances speed and accuracy for urban sensor forecasts by combining a closed-form graph-heat prior with a learned denoising diffusion model. The prior provides a smooth, parameter-free forecast, while the denoiser refines this structure. This composition allows the method to generate fast, accurate predictive distributions suitable for real-time alerting.