  Spatio-temporal graph neural networks (STGNN) have emerged as the dominant
model for spatio-temporal graph (STG) forecasting. Despite their success, they
fail to model intrinsic uncertainties within STG data, which cripples their
practicality in downstream tasks for decision-making. To this end, this paper
focuses on probabilistic STG forecasting, which is challenging due to the
difficulty in modeling uncertainties and complex ST dependencies. In this
study, we present the first attempt to generalize the popular denoising
diffusion probabilistic models to STGs, leading to a novel non-autoregressive
framework called DiffSTG, along with the first denoising network UGnet for STG
in the framework. Our approach combines the spatio-temporal learning
capabilities of STGNNs with the uncertainty measurements of diffusion models.
Extensive experiments validate that DiffSTG reduces the Continuous Ranked
Probability Score (CRPS) by 4%-14%, and Root Mean Squared Error (RMSE) by 2%-7%
over existing methods on three real-world datasets.
