{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:52Z","timestamp":1758672892005,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/394","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"3543-3551","source":"Crossref","is-referenced-by-count":0,"title":["CAN-ST: Clustering Adaptive Normalization for Spatio-temporal OOD Learning"],"prefix":"10.24963","author":[{"given":"Min","family":"Yang","sequence":"first","affiliation":[{"name":"Shandong University"}]},{"given":"Yang","family":"An","sequence":"additional","affiliation":[{"name":"Shandong University"}]},{"given":"Jinliang","family":"Deng","sequence":"additional","affiliation":[{"name":"HKGAI, Hong Kong University of Science and Technology"},{"name":"Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology"}]},{"given":"Xiaoyu","family":"Li","sequence":"additional","affiliation":[{"name":"Shandong University"}]},{"given":"Bin","family":"Xu","sequence":"additional","affiliation":[{"name":"Shandong University"}]},{"given":"Ji","family":"Zhong","sequence":"additional","affiliation":[{"name":"Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co., Ltd."}]},{"given":"Xiankai","family":"Lu","sequence":"additional","affiliation":[{"name":"Shandong University"}]},{"given":"Yongshun","family":"Gong","sequence":"additional","affiliation":[{"name":"Shandong University"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:55Z","timestamp":1758627235000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/394"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/394","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}