Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention

• URL: http://arxiv.org/abs/2204.11008v2
• Date: Wed, 27 Apr 2022 04:49:20 GMT
• Title: Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention
• Authors: Wei Shao, Zhiling Jin, Shuo Wang, Yufan Kang, Xiao Xiao, Hamid Menouar, Zhaofeng Zhang, Junshan Zhang, Flora Salim
• Abstract summary: Long-term tensor-temporal forecasting (LSTF) makes use of long-term dependency between spatial and temporal domains, contextual information, and inherent pattern in the data. We propose new graph models to represent the contextual information of each node and the long-term parking revealed-temporal data dependency structure. Our proposed approaches significantly improve the performance of existing graph neural network models in LSTF prediction tasks.
• Score: 20.52864145999387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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