{"type":"ScholarlyArticle","title":"Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning","authors":[{"name":"Jiewen Deng","slug":"jiewen-deng","given":"Jiewen","family":"Deng","affiliation":"Southern University of Science and Technology"},{"name":"Renhe Jiang","slug":"renhe-jiang","given":"Renhe","family":"Jiang","affiliation":"The University of Tokyo"},{"name":"Jiaqi Zhang","slug":"jiaqi-zhang","given":"Jiaqi","family":"Zhang","affiliation":"Southern University of Science and Technology"},{"name":"Xuan Song","slug":"xuan-song","given":"Xuan","family":"Song","affiliation":"Southern University of Science and Technology; Jilin University"}],"abstract":"Multi-modality spatio-temporal (MoST) data extends spatio-temporal (ST) data by incorporating multiple modalities,  which is prevalent in monitoring systems, encompassing diverse traffic demands and air quality assessments. Despite significant strides in ST modeling in recent years, there remains a need to emphasize harnessing the potential of information from different modalities. Robust MoST forecasting is more challenging because it possesses (i) high-dimensional and complex internal structures and (ii) dynamic heterogeneity caused by temporal, spatial, and modality variations. In this study, we propose a novel MoST learning framework via Self-Supervised Learning, namely MoSSL, which aims to uncover latent patterns from temporal, spatial, and modality perspectives while quantifying dynamic heterogeneity. Experiment results on two real-world MoST datasets verify the superiority of our approach compared with the state-of-the-art baselines. Model implementation is available at https://github.com/beginner-sketch/MoSSL.","doi":"10.24963/ijcai.2024/223","url":"https://ijcai.org/proceedings/2024/223","pdf":"https://ijcai.org/proceedings/2024/0223.pdf","markdown":"https://ijcai.org/proceedings/2024/223.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/223","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":223,"pages":{"first":2018,"last":2026},"keywords":["Machine Learning: ML: Time series and data streams","Knowledge Representation and Reasoning: KRR: Qualitative, geometric, spatial, and temporal reasoning","Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2020] Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 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