{"type":"ScholarlyArticle","title":"CAN-ST: Clustering Adaptive Normalization for Spatio-temporal OOD Learning","authors":[{"name":"Min Yang","slug":"min-yang","given":"Min","family":"Yang","affiliation":""},{"name":"Yang An","slug":"yang-an","given":"Yang","family":"An","affiliation":""},{"name":"Jinliang Deng","slug":"jinliang-deng","given":"Jinliang","family":"Deng","affiliation":""},{"name":"Xiaoyu Li","slug":"xiaoyu-li","given":"Xiaoyu","family":"Li","affiliation":""},{"name":"Bin Xu","slug":"bin-xu","given":"Bin","family":"Xu","affiliation":""},{"name":"Ji Zhong","slug":"ji-zhong","given":"Ji","family":"Zhong","affiliation":""},{"name":"Xiankai Lu","slug":"xiankai-lu","given":"Xiankai","family":"Lu","affiliation":""},{"name":"Yongshun Gong","slug":"yongshun-gong","given":"Yongshun","family":"Gong","affiliation":""}],"abstract":"Spatio-temporal data mining is crucial for decision-making and planning in diverse domains. However, in real-world scenarios, training and testing data are often not independent or identically distributed due to rapid changes in data distributions over time and space, resulting in spatio-temporal out-of-distribution (OOD) challenges. This non-stationarity complicates accurate predictions and has motivated research efforts focused on mitigating non-stationarity through normalization operations. Existing methods, nonetheless, often address individual time series in isolation, neglecting correlations across series, which limits their capacity to handle complex spatio-temporal dynamics and results in suboptimal solutions. To overcome these challenges, we propose Clustering Adaptive Normalization (CAN-ST), a general and model-agnostic method that mitigates non-stationarity by capturing both localized distributional changes and shared patterns across nodes via adaptive clustering and a parameter register. As a plugin, CAN-ST can be easily integrated into various spatio-temporal prediction models. Extensive experiments on multiple datasets with diverse forecasting models demonstrate that CAN-ST consistently improves performance by over 20% on average and outperforms state-of-the-art normalization methods.","doi":"10.24963/ijcai.2025/394","url":"https://ijcai.org/proceedings/2025/394","pdf":"https://ijcai.org/proceedings/2025/0394.pdf","markdown":"https://ijcai.org/proceedings/2025/394.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/394","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Data Mining","track":"Main Track","number":394,"pages":{"first":3543,"last":3551},"keywords":["Data Mining: DM: Mining data streams","Data Mining: DM: Applications","Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}