Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency between spatial and temporal domains, contextual information, and inherent pattern in the data. Recent studies have revealed the potential of multi-graph neural networks (MGNNs) to improve prediction performance. However, existing MGNN methods cannot be directly applied to LSTF due to several issues: the low level of generality, insufficient use of contextual information, and the imbalanced graph fusion approach. To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure. To fuse the information across multiple graphs, we propose a new dynamic multi-graph fusion module to characterize the correlations of nodes within a graph and the nodes across graphs via the spatial attention and graph attention mechanisms. Furthermore, we introduce a trainable weight tensor to indicate the importance of each node in different graphs. Extensive experiments on two large-scale datasets demonstrate that our proposed approaches significantly improve the performance of existing graph neural network models in LSTF prediction tasks.

翻译：许多现实世界无处不在的应用,例如停车建议和空气污染监测,都大大受益于准确的长期时空预报(LSTF)。LSTF利用了数据中空间和时间领域、背景信息和固有模式之间的长期依赖性,LSTF利用了数据中的时间空间和时间领域、背景信息和内在模式之间的长期依赖性。最近的研究揭示了多图形神经网络(MGNNs)在改善预测性能方面的潜力。然而,现有的MGNN方法不能直接适用于LSTF, 原因是几个问题:一般程度低、背景信息使用不足、以及图形融合方法不平衡。为了解决这些问题,我们建造了新的图形模型模型, 以代表每个节点的背景信息以及长期的时空数据依赖性结构。为了将信息整合到多个图中,我们提议了一个新的动态多图形融合模块, 以通过空间关注和图形关注机制来描述图中节点的相互关系。此外,我们引入了一种可训练的重度高度, 以显示不同图表中每个节点的重要性。关于两个大型数据网络模型的大规模实验,以显示我们现有的图像模型的运行情况。