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In this paper, we present a taxonomy of anomaly detection methods based on the main features, i.e., data dimension, processing technique, and anomaly type and six inner classes. We perform systematic intra- and inter-class comparisons of seventeen state-of-the-art algorithms on real and synthetic datasets with a point metric commonly used in classification problems and a range metric specifically designed for subsequence anomalies in time series data. We analyze the properties of these algorithms and test them in terms of effectiveness, efficiency, and robustness to anomaly rates, data sizes, number of dimensions, anomaly patterns, and threshold settings. We also test their performance in different use cases. Finally, we provide a practical guide for detecting anomalies in time series and discussions.<\/jats:p>","DOI":"10.14778\/3632093.3632110","type":"journal-article","created":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T11:26:31Z","timestamp":1705749991000},"page":"483-496","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["An Experimental Evaluation of Anomaly Detection in Time Series"],"prefix":"10.14778","volume":"17","author":[{"given":"Aoqian","family":"Zhang","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology"}]},{"given":"Shuqing","family":"Deng","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology"}]},{"given":"Dongping","family":"Cui","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology"}]},{"given":"Ye","family":"Yuan","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology"}]},{"given":"Guoren","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology"}]}],"member":"320","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Ahmed Abdulaal Zhuanghua Liu and Tomer Lancewicki. 2021. 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