Double-Diffusion: Balancing Speed, Accuracy, and Uncertainty in Probabilistic Forecasting for Urban Sensor Networks
Read the original on arXiv Machine Learning →Double‑Diffusion is a probabilistic forecasting framework for urban sensor networks that blends a closed‑form graph‑heat prior with a denoising diffusion model. The prior provides a low‑pass, parameter‑free forecast that both serves as the residual target and conditions the denoiser, allowing the reverse diffusion process to start near the prior and refine it over a short chain. Experiments on four real‑world air‑quality and traffic datasets show that Double‑Diffusion achieves the lowest CRPS among probabilistic methods while remaining competitive in point accuracy and requiring far fewer sampling steps than traditional from‑noise diffusion models.
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