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Surv."],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>\n                    Time series anomaly detection presents various challenges due to the sequential and dynamic nature of time-dependent data. Traditional unsupervised methods frequently encounter difficulties in generalization, often overfitting to known normal patterns observed during training and struggling to adapt to unseen normality. In response to this limitation, self-supervised techniques for time series have garnered attention as a potential solution to undertake this obstacle and enhance the performance of anomaly detectors. This article presents a comprehensive review of the recent methods that make use of self-supervised learning for time series anomaly detection. A taxonomy is proposed to categorize these methods based on their primary characteristics, facilitating a clear understanding of their diversity within this field. The information contained in this survey, along with additional details that will be periodically updated, is available on the following GitHub repository:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/Aitorzan3\/Awesome-Self-Supervised-Time-Series-Anomaly-Detection\">https:\/\/github.com\/Aitorzan3\/Awesome-Self-Supervised-Time-Series-Anomaly-Detection<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3770575","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T10:58:31Z","timestamp":1759489111000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["A Review on Self-Supervised Learning in Time Series Anomaly Detection: Recent Advances and Open Challenges"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6127-0686","authenticated-orcid":false,"given":"Aitor","family":"S\u00e1nchez-Ferrera","sequence":"first","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV\/EHU","place":["Donostia-San Sebastian, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9969-9664","authenticated-orcid":false,"given":"Borja","family":"Calvo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV\/EHU","place":["Donostia, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4683-8111","authenticated-orcid":false,"given":"Jose A.","family":"Lozano","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV\/EHU","place":["Donostia, Spain"]},{"name":"Basque Center for Applied Mathematics (BCAM)","place":["Donostia, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.09.051"},{"key":"e_1_3_3_3_2","first-page":"1","volume-title":"Proceedings of the Outlier Analysis","author":"Aggarwal Charu C.","year":"2016","unstructured":"Charu C. 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