{"type":"ScholarlyArticle","title":"Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention","authors":[{"name":"Wei Shao","slug":"wei-shao","given":"Wei","family":"Shao","affiliation":"School of Electrical, Computer and Energy Engineering , Arizona State University , Tempe , United States"},{"name":"Zhiling Jin","slug":"zhiling-jin","given":"Zhiling","family":"Jin","affiliation":"School of Telecommunications Engineering , Xidian University , Xi'an , China"},{"name":"Shuo Wang","slug":"shuo-wang","given":"Shuo","family":"Wang","affiliation":"D-ITET , ETH Zurich , Zurich , Switzerland"},{"name":"Yufan Kang","slug":"yufan-kang","given":"Yufan","family":"Kang","affiliation":"School of Computing Technologies , RMIT University , Melbourne , Australia"},{"name":"Xiao Xiao","slug":"xiao-xiao","given":"Xiao","family":"Xiao","affiliation":"School of Telecommunications Engineering , Xidian University , Xi'an , China"},{"name":"Hamid Menouar","slug":"hamid-menouar","given":"Hamid","family":"Menouar","affiliation":"Qatar Mobility Innovations Center , Qatar University , Doha , Qatar"},{"name":"Zhaofeng Zhang","slug":"zhaofeng-zhang","given":"Zhaofeng","family":"Zhang","affiliation":"School of Electrical, Computer and Energy Engineering , Arizona State University , Tempe , United States"},{"name":"Junshan Zhang","slug":"junshan-zhang","given":"Junshan","family":"Zhang","affiliation":"ECE Department at University of California Davis , Davis , United States"},{"name":"Flora Salim","slug":"flora-salim","given":"Flora","family":"Salim","affiliation":"School of Computer Science and Engineering , University of New South Wales , Sydney , Australia"}],"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 structure between the spatial and temporal domains, as well as the contextual information. Recent studies have revealed the potential of multi-graph neural networks (MGNNs) to improve prediction performance. However, existing MGNN methods do not work well when 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 exploit the long-term spatio-temporal data dependency structure. To aggregate 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.","doi":"10.24963/ijcai.2022/309","url":"https://ijcai.org/proceedings/2022/309","pdf":"https://ijcai.org/proceedings/2022/0309.pdf","markdown":"https://ijcai.org/proceedings/2022/309.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/309","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":309,"pages":{"first":2225,"last":2232},"keywords":["Data Mining: Mining Spatial and/or Temporal Data","Multidisciplinary Topics and Applications: Smart Cities"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986671","length_sec":87,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984814","length_sec":327,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bruna et al., 2013] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 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