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.

翻译：摘要：时空图神经网络已成为时空图预测的主导模型。尽管取得了成功，但它们无法建模时空数据中固有的不确定性，这削弱了其在决策相关下游任务中的实用性。为此，本文聚焦于概率时空图预测，其挑战在于不确定性建模及复杂时空依赖关系的处理。本研究首次将流行的去噪扩散概率模型推广至时空图，提出名为DiffSTG的新型非自回归框架，并在此框架中设计了首个针对时空图的去噪网络UGnet。该方法融合了时空图神经网络的时空学习能力与扩散模型的不确定性度量能力。大量实验表明，在三个真实数据集上，DiffSTG的连续排名概率评分降低4%-14%，均方根误差降低2%-7%，优于现有方法。