{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T18:30:35Z","timestamp":1781116235515,"version":"3.54.1"},"reference-count":14,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>Detecting anomalous subsequences in time series is an important task in time series analytics because it serves the identification of special events, such as production faults, delivery bottlenecks, system defects, or heart flicker. Consequently, many algorithms have been developed for the automatic detection of such anomalous patterns. The enormous number of approaches (i. e., more than 158 as of today), the lack of properly labeled test data, and the complexity of time series anomaly benchmarking have, though, led to a situation where choosing the best detection technique for a given anomaly detection task is a difficult challenge.<\/jats:p>\n          <jats:p>In this demonstration, we present TimeEval, an extensible, scalable and automatic benchmarking toolkit for time series anomaly detection algorithms. TimeEval includes an extensive data generator and supports both interactive and batch evaluation scenarios. With our novel toolkit, we aim to ease the evaluation effort and help the community to provide more meaningful evaluations.<\/jats:p>","DOI":"10.14778\/3554821.3554873","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3678-3681","source":"Crossref","is-referenced-by-count":46,"title":["TimeEval"],"prefix":"10.14778","volume":"15","author":[{"given":"Phillip","family":"Wenig","sequence":"first","affiliation":[{"name":"University of Potsdam, Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Schmidl","sequence":"additional","affiliation":[{"name":"University of Potsdam, Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thorsten","family":"Papenbrock","sequence":"additional","affiliation":[{"name":"Philipps University of Marburg, Marburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2017.2757953"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(96)00142-2"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972795.36"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143874"},{"key":"e_1_2_1_5_1","unstructured":"Docker Inc. 2022. Empowering App Development for Developers | Docker. https:\/\/www.docker.com  Docker Inc. 2022. Empowering App Development for Developers | Docker. https:\/\/www.docker.com"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.143.1.7063747"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML). 454--463","author":"Kadous Mohammed Waleed","year":"1999","unstructured":"Mohammed Waleed Kadous . 1999 . Learning Comprehensible Descriptions of Multivariate Time Series . In Proceedings of the International Conference on Machine Learning (ICML). 454--463 . Mohammed Waleed Kadous. 1999. Learning Comprehensible Descriptions of Multivariate Time Series. In Proceedings of the International Conference on Machine Learning (ICML). 454--463."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.3233\/978-1-61499-649-1-87"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/65943.65945"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3538598.3538602"},{"key":"e_1_2_1_11_1","unstructured":"Streamlit Inc. 2022. Streamlit \u2022 The fastest way to build and share data apps. https:\/\/streamlit.io  Streamlit Inc. 2022. Streamlit \u2022 The fastest way to build and share data apps. https:\/\/streamlit.io"},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of the International Conference on Neural Information Processing Systems (NeurIPS). 1920--1930","author":"Tatbul Nesime","year":"2018","unstructured":"Nesime Tatbul , Tae Jun Lee , Stan Zdonik , Mejbah Alam , and Justin Gottschlich . 2018 . Precision and Recall for Time Series . In Proceedings of the International Conference on Neural Information Processing Systems (NeurIPS). 1920--1930 . Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, and Justin Gottschlich. 2018. Precision and Recall for Time Series. In Proceedings of the International Conference on Neural Information Processing Systems (NeurIPS). 1920--1930."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2044967"},{"key":"e_1_2_1_14_1","volume-title":"Keogh","author":"Wu Renjie","year":"2020","unstructured":"Renjie Wu and Eamonn J . Keogh . 2020 . Current Time Series Anomaly Detection Benchmarks Are Flawed and Are Creating the Illusion of Progress . arXiv:2009.13807 [cs, stat] http:\/\/arxiv.org\/abs\/2009.13807 Renjie Wu and Eamonn J. Keogh. 2020. Current Time Series Anomaly Detection Benchmarks Are Flawed and Are Creating the Illusion of Progress. arXiv:2009.13807 [cs, stat] http:\/\/arxiv.org\/abs\/2009.13807"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3554821.3554873","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:34:13Z","timestamp":1672227253000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3554821.3554873"}},"subtitle":["a benchmarking toolkit for time series anomaly detection algorithms"],"short-title":[],"issued":{"date-parts":[[2022,8]]},"references-count":14,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.14778\/3554821.3554873"],"URL":"https:\/\/doi.org\/10.14778\/3554821.3554873","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8]]}}}