{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:25:32Z","timestamp":1763018732993,"version":"3.44.0"},"reference-count":75,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Hong Kong Innovation and Technology Commission","award":["ClMDA"],"award-info":[{"award-number":["ClMDA"]}]},{"name":"Hong Kong Research Grants Council","award":["21200522"],"award-info":[{"award-number":["21200522"]}]},{"name":"Chow Sang Sang Donation and Matching Fund","award":["9229161"],"award-info":[{"award-number":["9229161"]}]},{"name":"Sichuan Science and Technology Fund","award":["2025ZNSFSC0511","2025YFHZ0063"],"award-info":[{"award-number":["2025ZNSFSC0511","2025YFHZ0063"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1109\/tbdata.2025.3527215","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T15:31:22Z","timestamp":1736350282000},"page":"2338-2352","source":"Crossref","is-referenced-by-count":1,"title":["Generalized Time Series Classification via Component Decomposition and Alignment"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3705-5437","authenticated-orcid":false,"given":"Yichuan","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong"}]},{"given":"Darrick","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Mathematics, University of Edinburgh, Edinburgh, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2644-8906","authenticated-orcid":false,"given":"Harald","family":"Oberhauser","sequence":"additional","affiliation":[{"name":"Mathematical Institute, University of Oxford, Oxford, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8723-8112","authenticated-orcid":false,"given":"Haoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong"}]}],"member":"263","reference":[{"key":"ref1","first-page":"437","article-title":"A public domain dataset for human activity recognition using smartphones","volume-title":"Proc. Eur. Symp. Artif. Neural Netw.","author":"Anguita"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2468589"},{"article-title":"The UEA multivariate time series classification archive, 2018,","year":"2018","author":"Bagnall","key":"ref3"},{"key":"ref4","first-page":"1","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Wu"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467231"},{"key":"ref6","first-page":"1","article-title":"Omni-scale CNNs: A simple and effective kernel size configuration for time series classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Tang"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01026"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7502-7_79-1"},{"key":"ref10","first-page":"7728","article-title":"Efficient domain generalization via common-specific low-rank decomposition","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Piratla"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2615469"},{"key":"ref12","first-page":"5815","article-title":"Out-of-distribution generalization via risk extrapolation (REx)","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Krueger"},{"key":"ref13","first-page":"1","article-title":"Out-of-distribution representation learning for time series classification","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Lu"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2009.0502"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339579"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623613"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132980"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-016-0483-9"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3001377"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6165"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00701-z"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00844-1"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977172.23"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i14.29501"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i13.29317"},{"article-title":"Diffeomorphic temporal alignment nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shapira Weber","key":"ref26"},{"article-title":"Residual networks as flows of velocity fields for diffeomorphic time series alignment","year":"2021","author":"Huang","key":"ref27"},{"key":"ref28","first-page":"15122","article-title":"Closed-form diffeomorphic transformations for time series alignment","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Martinez"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3182382"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-06057-9"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00679-8"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00710-y"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00621"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00807"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00671"},{"key":"ref37","first-page":"1","article-title":"Domain-invariant feature exploration for domain generalization","volume":"2020","author":"Lu","year":"2022","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref38","first-page":"3915","article-title":"Feature-critic networks for heterogeneous domain generalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref39","first-page":"1006","article-title":"MetaReg: Towards domain generalization using meta-regularization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Balaji"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i13.17416"},{"key":"ref41","first-page":"12746","article-title":"Domain adaptation for time series under feature and label shifts","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"He"},{"key":"ref42","first-page":"10280","article-title":"Domain adaptation for time series forecasting via attention sharing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jin"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/285"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108616"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3080516"},{"key":"ref46","first-page":"21189","article-title":"Exploiting domain-specific features to enhance domain generalization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bui"},{"key":"ref47","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"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645593"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2288675"},{"key":"ref51","first-page":"873","article-title":"Empirical mode decomposition: Theory & applications","volume":"7","author":"Maheshwari","year":"2014","journal-title":"Int. J. Electron. Eng."},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2016.09.007"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3130234"},{"article-title":"Unsupervised scalable representation learning for multivariate time series","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Franceschi","key":"ref54"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487633"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-83508-8_23"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.110463"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110670"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605337-29"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2814042"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2416723"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/2809695.2809718"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/S1389-9457(02)00003-5"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1161\/01.CTR.101.23.e215"},{"key":"ref67","first-page":"1","article-title":"WOODS: Benchmarks for out-of-distribution generalization in time series","volume":"2023","author":"Gagnon-Audet","year":"2023","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1145\/3587937"},{"key":"ref69","first-page":"1","article-title":"Distributionally robust neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Sagawa"},{"key":"ref70","first-page":"1","article-title":"Learning explanations that are hard to vary","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Parascandolo"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539134"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497510"},{"issue":"11","key":"ref74","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299173"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6687317\/11149634\/10833669.pdf?arnumber=10833669","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T04:37:29Z","timestamp":1756960649000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10833669\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":75,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2025.3527215","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"type":"electronic","value":"2332-7790"},{"type":"electronic","value":"2372-2096"}],"subject":[],"published":{"date-parts":[[2025,10]]}}}