{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T17:08:39Z","timestamp":1767978519722,"version":"3.49.0"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["62225302"],"award-info":[{"award-number":["62225302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["92167108"],"award-info":[{"award-number":["92167108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["62173023"],"award-info":[{"award-number":["62173023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1109\/tnnls.2023.3287318","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T17:26:04Z","timestamp":1687973164000},"page":"15494-15506","source":"Crossref","is-referenced-by-count":2,"title":["Temporal\u2013Frequency Attention Focusing for Time Series Extrinsic Regression via Auxiliary Task"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6346-6930","authenticated-orcid":false,"given":"Lei","family":"Ren","sequence":"first","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1468-5659","authenticated-orcid":false,"given":"Tingyu","family":"Mo","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5644-7109","authenticated-orcid":false,"given":"Xuejun","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3023-1662","authenticated-orcid":false,"given":"Xi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3137178"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3136768"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3046629"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3084249"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3010524"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2651018"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2351391"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-021-00745-9"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1023"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1085"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467401"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2013.10.009"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00647-x"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2013.1071"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3157723"},{"key":"ref16","first-page":"1","article-title":"Support vector regression machines","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"9","author":"Drucker"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.29172\/7c2a6982-6d72-4cd8-bba6-2fccb06a7011"},{"key":"ref18","volume-title":"Machine learning benchmarks and random forest regression","author":"Segal","year":"2004"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00710-y"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379606734"},{"key":"ref23","article-title":"An overview of multi-task learning in deep neural networks","author":"Ruder","year":"2017","journal-title":"arXiv:1706.05098"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.5220\/0007684100002108"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00690"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2014.2351376"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-019-0103-9"},{"key":"ref28","first-page":"1","article-title":"Urban water quality prediction based on multi-task multi-view learning","volume-title":"Proc. 25th Int. Joint Conf. Artif. Intell.","author":"Liu"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2945999"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3314389"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00207"},{"key":"ref32","first-page":"15031","article-title":"Learning to select best forecast tasks for clinical outcome prediction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Xue"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/N15-1092"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32239-7_75"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.12.062"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-017-0677-5"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220060"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00701-z"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10737991\/10167683.pdf?arnumber=10167683","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T19:27:26Z","timestamp":1732735646000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10167683\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":39,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2023.3287318","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11]]}}}