Double-Diffusion: ODE-Prior Accelerated Diffusion Models for Spatio-Temporal Graph Forecasting

ArXi:2506.23053v2 Announce Type: replace Forecasting over graph-structured sensor networks demands models that capture both deterministic spatial trends and stochastic variability, while remaining efficient enough for repeated inference as new observations arrive. We propose Double-Diffusion, a denoising diffusion probabilistic model that integrates a parameter-free graph diffusion Ordinary Differential Equation (ODE) forecast as a structural prior throughout the generative process.