{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:35:48Z","timestamp":1783096548570,"version":"3.54.6"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&D Program of China","award":["2021ZD0111501"],"award-info":[{"award-number":["2021ZD0111501"]}]},{"DOI":"10.13039\/100014717","name":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62122022"],"award-info":[{"award-number":["62122022"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876043"],"award-info":[{"award-number":["61876043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976052"],"award-info":[{"award-number":["61976052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"PCL","award":["PCL2021A12"],"award-info":[{"award-number":["PCL2021A12"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1109\/tpami.2023.3304354","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T17:25:01Z","timestamp":1691774701000},"page":"1932-1949","source":"Crossref","is-referenced-by-count":35,"title":["Transferable Time-Series Forecasting Under Causal Conditional Shift"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3964-3789","authenticated-orcid":false,"given":"Zijian","family":"Li","sequence":"first","affiliation":[{"name":"School of Computing, Guangdong University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8972-167X","authenticated-orcid":false,"given":"Ruichu","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3312-2402","authenticated-orcid":false,"given":"Tom Z. J.","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9731-1504","authenticated-orcid":false,"given":"Zhifeng","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Science, Shantou University, Shantou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0738-9958","authenticated-orcid":false,"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref2","first-page":"7404","article-title":"Bridging theory and algorithm for domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00116"},{"key":"ref4","first-page":"24791","article-title":"Domain adaptation with invariant representation learning: What transformations to learn?","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Stojanov"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3631712"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.11.017"},{"key":"ref7","first-page":"9638","article-title":"Aggregating from multiple target-shifted sources","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Shui"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3445033"},{"key":"ref9","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"ref11","first-page":"6859","article-title":"Time series domain adaptation via sparse associative structure alignment","volume-title":"Proc. AAAI Conf. Artif. Intell.","author":"Cai"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/285"},{"key":"ref13","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ganin"},{"key":"ref14","first-page":"5423","article-title":"Learning semantic representations for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xie"},{"key":"ref15","first-page":"819","article-title":"Domain adaptation under target and conditional shift","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref16","first-page":"4965","article-title":"Domain adaptation as a problem of inference on graphical models","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2019.106682"},{"key":"ref18","article-title":"Variational recurrent adversarial deep domain adaptation","author":"Purushotham","year":"2017","journal-title":"Proc. Int. Conf. Learn. Representations"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9542"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00814"},{"key":"ref21","first-page":"2980","article-title":"A recurrent latent variable model for sequential data","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Chung"},{"key":"ref22","first-page":"10 280","article-title":"Domain adaptation for time series forecasting via attention sharing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jin"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jedc.2005.08.008"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.2307\/1912791.1969"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.1667"},{"key":"ref26","article-title":"Interpretable models for Granger Causality using self-explaining neural networks","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Marcinkevics"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3065601"},{"key":"ref28","article-title":"Amortized causal discovery: Learning to infer causal graphs from time-series data","volume-title":"Proc. 1st Conf. Causal Learn. Reasoning","author":"L\u00f6we"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-10105-3"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2008.02.002"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1523\/jneurosci.4399-14.2015"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557085"},{"key":"ref33","article-title":"Time series analysis","volume-title":"The Oxford Handbook of Quantitative Methods in Psychology","volume":"2","author":"Wei","year":"2006"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00532.x"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-27752-1"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1177\/0049124198027002004"},{"key":"ref38","article-title":"Auto-encoding variational bayes","volume-title":"Proc. 2nd Int. Conf. Learn. Representations","author":"Kingma","year":"2014"},{"key":"ref39","article-title":"Categorical reparameterization with Gumbel-Softmax","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Jang"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2017.01.003"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87722-4_2"},{"key":"ref42","article-title":"Unrolled generative adversarial networks","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Metz"},{"key":"ref43","volume-title":"Foundations of Machine Learning","author":"Mohri","year":"2018"},{"key":"ref44","article-title":"Domain adaptation: Learning bounds and algorithms","volume-title":"Proc. 22nd Conf. Learn. Theory","author":"Mansour"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24412-4_25"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0022"},{"key":"ref47","article-title":"Deep domain confusion: Maximizing for domain invariance","author":"Tzeng","year":"2014"},{"key":"ref48","first-page":"22419","article-title":"Autoformer: Decomposition transformers with Auto-Correlation for long-term series forecasting","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref49","first-page":"35 971","article-title":"On the parameterization and initialization of diagonal state space models","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Gu"},{"key":"ref50","article-title":"Efficiently modeling long sequences with structured state spaces","author":"Gu","year":"2021"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10461350\/10214679.pdf?arnumber=10214679","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T02:20:33Z","timestamp":1709864433000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10214679\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":50,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3304354","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]}}}