{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:38:44Z","timestamp":1761176324499,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Multivariate time series forecasting (MTSF) is of immense significance in extensive domains, such as traffic analysis and weather forecasting. Despite the accuracy of MTSF has made significant progress in recent years, most research efforts typically require prohibitive spatio-temporal overhead, especially when dealing with higher-dimensional data. This limitation hinders the scalability of the model for real-world applications. To address this issue, we propose LSSM, a Low-Rank State Space Model that conducts multivariate time series forecasting at lower computation costs while effectively capturing intricate long-range dependencies and variable correlations within data. We apply the KL-constraint during training which enhances both the generalization of the model and the association between subsequences. Experiment results on four benchmark datasets verify the superiority of our approach compared with the state-of-the-art baselines.<\/jats:p>","DOI":"10.3233\/faia251483","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:04:08Z","timestamp":1761127448000},"source":"Crossref","is-referenced-by-count":0,"title":["Low-Rank State Space Model for Multivariate Time Series Forecasting"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9176-496X","authenticated-orcid":false,"given":"Yongrong","family":"Wu","sequence":"first","affiliation":[{"name":"The School of Informatics, Xiamen University"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7807-8586","authenticated-orcid":false,"given":"Honjin","family":"Chen","sequence":"additional","affiliation":[{"name":"The School of Informatics, Xiamen University"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3440-2149","authenticated-orcid":false,"given":"Hao","family":"Lan","sequence":"additional","affiliation":[{"name":"The School of Informatics, Xiamen University"}]},{"given":"Ruofan","family":"Ma","sequence":"additional","affiliation":[{"name":"The School of Informatics, Xiamen University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5338-0471","authenticated-orcid":false,"given":"Lvqing","family":"Yang","sequence":"additional","affiliation":[{"name":"The School of Informatics, Xiamen University"}]},{"given":"Jiahao","family":"Wu","sequence":"additional","affiliation":[{"name":"The School of Informatics, Xiamen University"}]},{"given":"Xinyan","family":"Shen","sequence":"additional","affiliation":[{"name":"The School of Informatics, Xiamen University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251483","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:04:09Z","timestamp":1761127449000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251483"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251483","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}