概率逻辑
自回归模型
计算机科学
图形
均方误差
统计模型
降噪
人工智能
数据挖掘
机器学习
数据建模
数学
理论计算机科学
统计
数据库
作者
Haomin Wen,Youfang Lin,Yutong Xia,Huaiyu Wan,Qingsong Wen,Roger Zimmermann,Yuxuan Liang
标识
DOI:10.1145/3589132.3625614
摘要
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 de-noising 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.
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