{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T18:37:25Z","timestamp":1773254245243,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Hong Kong RGC AOE Project","award":["AoE\/E-603\/18"],"award-info":[{"award-number":["AoE\/E-603\/18"]}]},{"name":"Hong Kong ITC ITF grants","award":["ITS\/044\/18FX, ITS\/470\/18FX"],"award-info":[{"award-number":["ITS\/044\/18FX, ITS\/470\/18FX"]}]},{"name":"China NSFC","award":["No. 61729201"],"award-info":[{"award-number":["No. 61729201"]}]},{"name":"Didi-HKUST joint research lab project"},{"name":"Hong Kong RGC CRF Project","award":["C6030-18G, C1031-18G, C5026-18G"],"award-info":[{"award-number":["C6030-18G, C1031-18G, C5026-18G"]}]},{"name":"Microsoft Research Asia Collaborative Research Grant"},{"name":"Hong Kong RGC GRF Project","award":["16207617"],"award-info":[{"award-number":["16207617"]}]},{"name":"Wechat and Webank Research Grants"},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2019B151530001"],"award-info":[{"award-number":["2019B151530001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,8]]},"DOI":"10.1145\/3437963.3441823","type":"proceedings-article","created":{"date-parts":[[2021,3,6]],"date-time":"2021-03-06T04:36:17Z","timestamp":1615005377000},"page":"824-832","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection"],"prefix":"10.1145","author":[{"given":"Jia","family":"Li","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Shimin","family":"Di","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Yanyan","family":"Shen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, China"}]}],"member":"320","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n.d.]. https:\/\/github.com\/lizeyan\/workshop.aiops.org\/raw\/master\/files\/2018\/logicmonitor2018.pdf. [n.d.]. https:\/\/github.com\/lizeyan\/workshop.aiops.org\/raw\/master\/files\/2018\/logicmonitor2018.pdf."},{"key":"e_1_3_2_1_2_1","unstructured":"[n.d.]. https:\/\/github.com\/lizeyan\/workshop.aiops.org\/raw\/master\/files\/2018\/d.i.2018.pdf. [n.d.]. https:\/\/github.com\/lizeyan\/workshop.aiops.org\/raw\/master\/files\/2018\/d.i.2018.pdf."},{"key":"e_1_3_2_1_3_1","unstructured":"[n.d.]. https:\/\/github.com\/lizeyan\/workshop.aiops.org\/raw\/master\/files\/2018\/ica1282018.pdf. [n.d.]. https:\/\/github.com\/lizeyan\/workshop.aiops.org\/raw\/master\/files\/2018\/ica1282018.pdf."},{"key":"e_1_3_2_1_4_1","unstructured":"[n.d.]. https:\/\/github.com\/DawnsonLi\/EVT. [n.d.]. https:\/\/github.com\/DawnsonLi\/EVT."},{"key":"e_1_3_2_1_5_1","unstructured":"[n.d.]. AIOps Challenge Final Dataset. http:\/\/iops.ai\/dataset_detail\/?id=10. [n.d.]. AIOps Challenge Final Dataset. http:\/\/iops.ai\/dataset_detail\/?id=10."},{"key":"e_1_3_2_1_6_1","unstructured":"[n.d.]. AIOps Challenge KPI Anomaly Detection Competition. http:\/\/iops.ai\/competition_detail\/?competition_id=5&flag=1. [n.d.]. AIOps Challenge KPI Anomaly Detection Competition. http:\/\/iops.ai\/competition_detail\/?competition_id=5&flag=1."},{"key":"e_1_3_2_1_7_1","unstructured":"[n.d.]. Yahoo! Webscope Dataset. A Labeled Anomaly Detection Dataset version 1.0. https:\/\/webscope.sandbox.yahoo.com\/catalog.php?datatype=s&did=70. [n.d.]. Yahoo! Webscope Dataset. A Labeled Anomaly Detection Dataset version 1.0. https:\/\/webscope.sandbox.yahoo.com\/catalog.php?datatype=s&did=70."},{"key":"e_1_3_2_1_8_1","volume-title":"Special Lecture on IE","volume":"2","author":"An Jinwon","year":"2015","unstructured":"Jinwon An and Sungzoon Cho . 2015 . Variational autoencoder based anomaly detection using reconstruction probability . Special Lecture on IE , Vol. 2 , 1 (2015). Jinwon An and Sungzoon Cho. 2015. Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE, Vol. 2, 1 (2015)."},{"key":"e_1_3_2_1_9_1","volume-title":"Statistics of extremes: theory and applications","author":"Beirlant Jan","unstructured":"Jan Beirlant , Yuri Goegebeur , Johan Segers , and Jozef L Teugels . 2006. Statistics of extremes: theory and applications . John Wiley & Sons . Jan Beirlant, Yuri Goegebeur, Johan Segers, and Jozef L Teugels. 2006. Statistics of extremes: theory and applications .John Wiley & Sons."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.2307\/2347162"},{"key":"e_1_3_2_1_11_1","volume-title":"2019 a. Sequential VAE-LSTM for Anomaly Detection on Time Series. arXiv preprint arXiv:1910.03818","author":"Chen Run-Qing","year":"2019","unstructured":"Run-Qing Chen , Guang-Hui Shi , Wan-Lei Zhao , and Chang-Hui Liang . 2019 a. Sequential VAE-LSTM for Anomaly Detection on Time Series. arXiv preprint arXiv:1910.03818 ( 2019 ). Run-Qing Chen, Guang-Hui Shi, Wan-Lei Zhao, and Chang-Hui Liang. 2019 a. Sequential VAE-LSTM for Anomaly Detection on Time Series. arXiv preprint arXiv:1910.03818 (2019)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737430"},{"key":"e_1_3_2_1_13_1","volume-title":"Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio.","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho , Bart Van Merri\u00ebnboer , Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 . Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014). Kyunghyun Cho, Bart Van Merri\u00ebnboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2379776.2379788"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2010.09.007"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.2514\/6.2016-2430"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2009.12.010"},{"key":"e_1_3_2_1_18_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation , Vol. 9 , 8 ( 1997 ), 1735--1780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219845"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1080\/00224065.1986.11979014"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788611"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2012.6195498"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2815675.2815679"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1155\/2009\/837601"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.3390\/s18051308"},{"key":"e_1_3_2_1_26_1","first-page":"2","article-title":"Anomaly detection in time series of graphs using arma processes","volume":"24","author":"Pincombe Brandon","year":"2005","unstructured":"Brandon Pincombe . 2005 . Anomaly detection in time series of graphs using arma processes . Asor Bulletin , Vol. 24 , 4 (2005), 2 . Brandon Pincombe. 2005. Anomaly detection in time series of graphs using arma processes. Asor Bulletin, Vol. 24, 4 (2005), 2.","journal-title":"Asor Bulletin"},{"key":"e_1_3_2_1_27_1","volume-title":"Time-Series Anomaly Detection Service at Microsoft. arXiv preprint arXiv:1906.03821","author":"Ren Hansheng","year":"2019","unstructured":"Hansheng Ren , Bixiong Xu , Yujing Wang , Chao Yi , Congrui Huang , Xiaoyu Kou , Tony Xing , Mao Yang , Jie Tong , and Qi Zhang . 2019. Time-Series Anomaly Detection Service at Microsoft. arXiv preprint arXiv:1906.03821 ( 2019 ). Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, and Qi Zhang. 2019. Time-Series Anomaly Detection Service at Microsoft. arXiv preprint arXiv:1906.03821 (2019)."},{"key":"e_1_3_2_1_28_1","volume-title":"Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082","author":"Rezende Danilo Jimenez","year":"2014","unstructured":"Danilo Jimenez Rezende , Shakir Mohamed , and Daan Wierstra . 2014. Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082 ( 2014 ). Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082 (2014)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1983.10487848"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098144"},{"key":"e_1_3_2_1_31_1","first-page":"58","article-title":"The problem of concept drift: definitions and related work","volume":"106","author":"Tsymbal Alexey","year":"2004","unstructured":"Alexey Tsymbal . 2004 . The problem of concept drift: definitions and related work . Computer Science Department, Trinity College Dublin , Vol. 106 , 2 (2004), 58 . Alexey Tsymbal. 2004. The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin, Vol. 106, 2 (2004), 58.","journal-title":"Computer Science Department, Trinity College Dublin"},{"key":"e_1_3_2_1_32_1","volume-title":"6th $$USENIX$$ Workshop on Hot Topics in Cloud Computing (HotCloud 14) .","author":"Vallis Owen","unstructured":"Owen Vallis , Jordan Hochenbaum , and Arun Kejariwal . 2014. A novel technique for long-term anomaly detection in the cloud . In 6th $$USENIX$$ Workshop on Hot Topics in Cloud Computing (HotCloud 14) . Owen Vallis, Jordan Hochenbaum, and Arun Kejariwal. 2014. A novel technique for long-term anomaly detection in the cloud. In 6th $$USENIX$$ Workshop on Hot Topics in Cloud Computing (HotCloud 14) ."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3185996"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Zhao Xu Kristian Kersting and Lorenzo von Ritter. 2017. Stochastic Online Anomaly Analysis for Streaming Time Series.. In IJCAI. 3189--3195. Zhao Xu Kristian Kersting and Lorenzo von Ritter. 2017. Stochastic Online Anomaly Analysis for Streaming Time Series.. In IJCAI. 3189--3195.","DOI":"10.24963\/ijcai.2017\/445"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCSN.2010.55"},{"key":"e_1_3_2_1_36_1","volume-title":"Label-Less: A Semi-Automatic Labelling Tool for KPI Anomalies. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE","author":"Zhao Nengwen","year":"2019","unstructured":"Nengwen Zhao , Jing Zhu , Rong Liu , Dapeng Liu , Ming Zhang , and Dan Pei . 2019 . Label-Less: A Semi-Automatic Labelling Tool for KPI Anomalies. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE , 1882--1890. Nengwen Zhao, Jing Zhu, Rong Liu, Dapeng Liu, Ming Zhang, and Dan Pei. 2019. Label-Less: A Semi-Automatic Labelling Tool for KPI Anomalies. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 1882--1890."},{"key":"e_1_3_2_1_37_1","volume-title":"Learning under concept drift: an overview. arXiv preprint arXiv:1010.4784","author":"Zliobaite Indre","year":"2010","unstructured":"Indre Zliobaite . 2010. Learning under concept drift: an overview. arXiv preprint arXiv:1010.4784 ( 2010 ). Indre Zliobaite. 2010. Learning under concept drift: an overview. arXiv preprint arXiv:1010.4784 (2010)."}],"event":{"name":"WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining","location":"Virtual Event Israel","acronym":"WSDM '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 14th ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441823","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3437963.3441823","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:36Z","timestamp":1750193256000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441823"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":37,"alternative-id":["10.1145\/3437963.3441823","10.1145\/3437963"],"URL":"https:\/\/doi.org\/10.1145\/3437963.3441823","relation":{},"subject":[],"published":{"date-parts":[[2021,3,8]]},"assertion":[{"value":"2021-03-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}