{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T15:42:03Z","timestamp":1782574923536,"version":"3.54.5"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272398"],"award-info":[{"award-number":["62272398"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Program","award":["2024NSFJQ0019"],"award-info":[{"award-number":["2024NSFJQ0019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1109\/tkde.2026.3658637","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:00:46Z","timestamp":1769634046000},"page":"2366-2379","source":"Crossref","is-referenced-by-count":3,"title":["Preference Guided Meta-Learning for Cross Domain Time Series Forecasting"],"prefix":"10.1109","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9650-130X","authenticated-orcid":false,"given":"Xingwang","family":"Li","sequence":"first","affiliation":[{"name":"Computing, Artificial Intelligence, Southwest Jiaotong University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9535-7245","authenticated-orcid":false,"given":"Fei","family":"Teng","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Southwest Jiaotong University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7780-104X","authenticated-orcid":false,"given":"Tianrui","family":"Li","sequence":"additional","affiliation":[{"name":"Computing, Artificial Intelligence, Southwest Jiaotong University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7832-1937","authenticated-orcid":false,"given":"Qiang","family":"Duan","sequence":"additional","affiliation":[{"name":"Information Sciences &#x0026; Technology Department, Pennsylvania State University, Abington, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3484454"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00101"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645434"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02227"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02218"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00113"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3344761"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.485"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3438259"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3435859"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3486349"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3292359"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.310"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3490843"},{"key":"ref16","first-page":"16405","article-title":"Large brain model for learning generic representations with tremendous EEG data in BCI","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Jiang","year":"2024"},{"key":"ref17","first-page":"78240","article-title":"BIOT: Biosignal transformer for cross-data learning in the wild","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yang","year":"2024"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.466"},{"key":"ref19","first-page":"10876","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Nie","year":"2023"},{"key":"ref20","article-title":"The bigger the better? Rethinking the effective model scale in long-term time series forecasting","author":"Deng","year":"2024"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3153663"},{"key":"ref22","first-page":"53728","article-title":"Direct preference optimization: Your language model is secretly a reward model","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rafailov","year":"2024"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3498346"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02134-3"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3357847"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3387317"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3475809"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113825"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3400008"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482315"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3643035"},{"key":"ref32","first-page":"18546","article-title":"TEMPO: Prompt-based generative pre-trained transformer for time series forecasting","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Cao","year":"2024"},{"key":"ref33","first-page":"43322","article-title":"One fits all: Power general time series analysis by pretrained LM","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhou","year":"2023"},{"key":"ref34","first-page":"23857","article-title":"Time-LLM: Time series forecasting by reprogramming large language models","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Jin","year":"2024"},{"key":"ref35","first-page":"60162","article-title":"Are language models actually useful for time series forecasting?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tan","year":"2025"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01310"},{"key":"ref37","first-page":"55204","article-title":"Contrastive preference optimization: Pushing the boundaries of LLM performance in machine translation","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Xu","year":"2024"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.626"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.52202\/079017-2359"},{"key":"ref40","first-page":"83314","article-title":"Scaling law for time series forecasting","volume-title":"Proc. 38th Annu. Conf. Neural Inf. Process. Syst.","author":"Shi","year":"2024"},{"issue":"1","key":"ref41","first-page":"3","article-title":"STL: A seasonal-trend decomposition","volume":"6","author":"Cleveland","year":"1990","journal-title":"J. Off. Stat."},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2012.737745"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0718"},{"key":"ref44","first-page":"27268","article-title":"FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhou","year":"2022"},{"key":"ref45","first-page":"22419","article-title":"AutoFormer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wu","year":"2021"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/11427039\/11367281.pdf?arnumber=11367281","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:39:08Z","timestamp":1773347948000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11367281\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":47,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2026.3658637","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]}}}