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We address deficiencies in current evaluation procedures related to datasets and experimental settings and protocols. Specifically, we propose a new time series anomaly detection benchmark, called TAB. First, TAB encompasses 29 public multivariate datasets and 1,635 univariate time series from different domains to facilitate more comprehensive evaluations on diverse datasets. Second, TAB covers a variety of TSAD methods, including Non-learning, Machine learning, Deep learning, LLM-based, and Time-series pre-trained methods. Third, TAB features a unified and automated evaluation pipeline that enables fair and easy evaluation of TSAD methods. Finally, we employ TAB to evaluate existing TSAD methods and report on the outcomes, thereby offering a deeper insight into the performance of these methods.<\/jats:p>","DOI":"10.14778\/3746405.3746407","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T17:06:20Z","timestamp":1756919180000},"page":"2775-2789","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["TAB: Unified Benchmarking of Time Series Anomaly Detection Methods"],"prefix":"10.14778","volume":"18","author":[{"given":"Xiangfei","family":"Qiu","sequence":"first","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Zhe","family":"Li","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Wanghui","family":"Qiu","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Shiyan","family":"Hu","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Lekui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Huawei Cloud Availability Engineering Lab, China"}]},{"given":"Xingjian","family":"Wu","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Zhengyu","family":"Li","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Chenjuan","family":"Guo","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Aoying","family":"Zhou","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Zhenli","family":"Sheng","sequence":"additional","affiliation":[{"name":"Huawei Cloud Availability Engineering Lab, China"}]},{"given":"Jilin","family":"Hu","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Christian S.","family":"Jensen","sequence":"additional","affiliation":[{"name":"Aalborg University, Denmark"}]},{"given":"Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]}],"member":"320","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Ahmed Abdulaal Zhuanghua Liu and Tomer Lancewicki. 2021. 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