{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T06:17:08Z","timestamp":1783664228045,"version":"3.55.0"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","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":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476126"],"award-info":[{"award-number":["62476126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1109\/tkde.2025.3589693","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T17:40:09Z","timestamp":1752687609000},"page":"5967-5982","source":"Crossref","is-referenced-by-count":2,"title":["iBACon: imBalance-Aware Contrastive Learning for Time Series Forecasting"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5955-2063","authenticated-orcid":false,"given":"Jing","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4618-7299","authenticated-orcid":false,"given":"Qun","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3312261"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3234130"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2954510"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2022.3160266"},{"key":"ref5","first-page":"1","article-title":"CoST: Contrastive learning of disentangled seasonal-trend representations for time series forecasting","volume-title":"Proc. 10th Int. Conf. Learn. Representations","author":"Woo"},{"key":"ref6","first-page":"71222","article-title":"Basisformer: Attention-based time series forecasting with learnable and interpretable basis","volume-title":"Proc. 37th Conf. Neural Inf. Process. Syst.","author":"Ni"},{"key":"ref7","first-page":"27268","article-title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Zhou"},{"key":"ref8","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3268118"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110415"},{"key":"ref11","volume-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"2015"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2020.06.008"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref14","first-page":"11842","article-title":"Delving into deep imbalanced regression","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Yang"},{"key":"ref15","first-page":"7634","article-title":"RankSim: Ranking similarity regularization for deep imbalanced regression","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Gong"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3090866"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3387317"},{"key":"ref18","first-page":"1","article-title":"CoDA: Contrast-enhanced and diversity-promoting data augmentation for natural language understanding","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Qu"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3172423"},{"key":"ref20","first-page":"16969","article-title":"Utilizing expert features for contrastive learning of time-series representations","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Nonnenmacher"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.03.143"},{"key":"ref22","first-page":"17882","article-title":"Rank-N-contrast: Learning continuous representations for regression","volume-title":"Proc. 37th Conf. Neural Inf. Process. Syst.","author":"Zha"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29299"},{"key":"ref24","first-page":"1","article-title":"Self-supervised contrastive learning for long-term forecasting","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Park"},{"key":"ref25","first-page":"1","article-title":"Unsupervised representation learning for time series with temporal neighborhood coding","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Tonekaboni"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/324"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467401"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"ref29","first-page":"25038","article-title":"Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Yang"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25575"},{"key":"ref31","first-page":"3988","article-title":"Self-supervised contrastive pre-training for time series via time-frequency consistency","volume-title":"Proc. 36th Conf. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3691338"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599295"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3335317"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110874"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.03.041"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119103"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126562"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3192475"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01290"},{"key":"ref41","first-page":"19290","article-title":"Rethinking the value of labels for improving class-imbalanced learning","volume-title":"Proc. 34th Conf. Neural Inf. Process. Syst.","author":"Yang"},{"key":"ref42","first-page":"1","article-title":"Exploring balanced feature spaces for representation learning","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Kang"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-017-0044-3"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06837-3"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43424-2_9"},{"key":"ref46","first-page":"1","article-title":"Decoupling representation and classifier for long-tailed recognition","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Kang"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.2307\/2332010"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref49","first-page":"1","article-title":"Reversible instance normalization for accurate time-series forecasting against distribution shift","volume-title":"Proc. 10th Int. Conf. Learn. Representations","author":"Kim"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref51","first-page":"1","article-title":"MICN: Multi-scale local and global context modeling for long-term series forecasting","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Wang"},{"key":"ref52","first-page":"1","article-title":"Parametric augmentation for time series contrastive learning","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Zheng"},{"key":"ref53","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Representations","author":"Kingma"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/11163535\/11081860.pdf?arnumber=11081860","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T05:42:14Z","timestamp":1757742134000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11081860\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":53,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2025.3589693","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":[[2025,10]]}}}