{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T21:37:37Z","timestamp":1768772257352,"version":"3.49.0"},"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>Market regimes are a critical factor influencing stock price fluctuations. We observe that regime characteristics can be reflected in the dynamic variations in the strength of multiple inter-stock relationships. However, existing methodologies predominantly rely on a single graph constructed using prior knowledge or directly infer a singular type of relationship from time series data. These approaches fail to account for the existence of multiple types of relationships and their dynamic variations in strength. To address this limitation, we propose a novel framework, the Dual-Path Adaptive-Correlation Spatial-Temporal Inverted Transformer (DPA-STIFormer), which decouples time series data to learn diverse types of relationships and introduces a gated mechanism to adaptively fuse them, thereby accommodating different market regimes. Experiments conducted on four stock market datasets demonstrate state-of-the-art performance, with an average improvement of over 5%, validating the model\u2019s superior capability in uncovering latent temporal-correlation patterns.<\/jats:p>","DOI":"10.3233\/faia251117","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:51:57Z","timestamp":1761126717000},"source":"Crossref","is-referenced-by-count":2,"title":["Dual-Path Adaptive-Correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecasting"],"prefix":"10.3233","author":[{"given":"Wenbo","family":"Yan","sequence":"first","affiliation":[{"name":"School of Intelligence Science and Technology, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shurui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Intelligence Science and Technology, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Intelligence Science and Technology, Peking University, Beijing 100871, China"},{"name":"Institute for Artificial Intellignce, Peking University, Beijing 100871, China"},{"name":"State Key Laboratory of General Artificial Intelligence, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251117","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:51:57Z","timestamp":1761126717000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251117","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]]}}}