{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:24:32Z","timestamp":1780053872530,"version":"3.54.0"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539339","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"635-645","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":70,"title":["Local Evaluation of Time Series Anomaly Detection Algorithms"],"prefix":"10.1145","author":[{"given":"Alexis","family":"Huet","sequence":"first","affiliation":[{"name":"Huawei Technologies Co., Ltd., Boulogne-Billancourt, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose Manuel","family":"Navarro","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd., Boulogne-Billancourt, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dario","family":"Rossi","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd., Boulogne-Billancourt, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.04.070"},{"key":"e_1_3_2_2_2_1","volume-title":"Encyclopedia of distances","author":"Deza Michel Marie","unstructured":"Michel Marie Deza and Elena Deza. 2009. Encyclopedia of distances. In Springer."},{"key":"e_1_3_2_2_3_1","first-page":"566","article-title":"A modified Hausdorff distance for object matching","volume":"1","author":"Dubuisson Marie-Pierre","year":"1994","unstructured":"Marie-Pierre Dubuisson and Anil K. Jain. 1994. A modified Hausdorff distance for object matching. In IEEE ICPR. Volume 1, 566--568.","journal-title":"IEEE ICPR."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13160-012-0089-6"},{"key":"e_1_3_2_2_5_1","article-title":"An evaluation of anomaly detection and diagnosis in multivariate time series","author":"Astha Garg","year":"2021","unstructured":"Astha Garg et al. 2021. An evaluation of anomaly detection and diagnosis in multivariate time series. IEEE Trans. on Neural Networks and Learning Systems.","journal-title":"IEEE Trans. on Neural Networks and Learning Systems."},{"key":"e_1_3_2_2_6_1","unstructured":"Andr\u00e9 Gensler and Bernhard Sick. 2014. Novel criteria to measure performance of time series segmentation techniques. In LWA. Citeseer 193--204."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Shaghayegh Gharghabi et al. 2017. Matrix profile VIII: domain agnostic online semantic segmentation at superhuman performance levels. In IEEE ICDM.","DOI":"10.1109\/ICDM.2017.21"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Jonathan Goh et al. 2016. A dataset to support research in the design of secure water treatment systems. In CRITIS. Springer 88--99.","DOI":"10.1007\/978-3-319-71368-7_8"},{"key":"e_1_3_2_2_9_1","unstructured":"Alexis Huet et al. 2022. Affiliation precision\/recall library. https:\/\/doi.org\/10. 6084\/m9.figshare.19131425. (2022)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Kyle Hundman et al. 2018. Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding. In ACM SIGKDD 387--395.","DOI":"10.1145\/3219819.3219845"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Won-Seok Hwang et al. 2019. Time-series aware precision and recall for anomaly detection: considering variety of detection result and addressing ambiguous labeling. In ACM CIKM 2241--2244.","DOI":"10.1145\/3357384.3358118"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Nick James et al. 2020. Novel semi-metrics for multivariate change point analysis and anomaly detection. Physica D: Nonlinear Phenomena 412.","DOI":"10.1016\/j.physd.2020.132636"},{"key":"e_1_3_2_2_13_1","unstructured":"Jonguk Kim et al. 2019. Anomaly detection for industrial control systems using sequence-to-sequence neural networks. CyberICPS."},{"key":"e_1_3_2_2_14_1","unstructured":"Siwon Kim et al. 2021. Towards a rigorous evaluation of time-series anomaly detection. arXiv preprint arXiv:2109.05257."},{"key":"e_1_3_2_2_15_1","unstructured":"Nikolay Laptev et al. 2015. S5 - a labeled anomaly detection dataset v1.0 (16M). https:\/\/webscope.sandbox.yahoo.com\/catalog.php?datatype=s&did=70. (2015)."},{"key":"e_1_3_2_2_16_1","volume-title":"Evaluating real-time anomaly detection algorithms ? the Numenta anomaly benchmark","author":"Lavin Alexander","unstructured":"Alexander Lavin and Subutai Ahmad. 2015. Evaluating real-time anomaly detection algorithms ? the Numenta anomaly benchmark. In IEEE ICMLA."},{"key":"e_1_3_2_2_17_1","unstructured":"Tae Jun Lee et al. 2018. Greenhouse: a zero-positive machine learning system for time-series anomaly detection. SysML."},{"key":"e_1_3_2_2_18_1","unstructured":"LinkedIn. 2018. Luminol. https:\/\/github.com\/linkedin\/luminol. (2018)."},{"key":"e_1_3_2_2_19_1","unstructured":"Fei Tony Liu et al. 2008. Isolation forest. In IEEE ICDM 413--422."},{"key":"e_1_3_2_2_20_1","first-page":"89","article-title":"Long short term memory networks for anomaly detection in time series","volume":"89","author":"Pankaj Malhotra","year":"2015","unstructured":"Pankaj Malhotra et al. 2015. Long short term memory networks for anomaly detection in time series. In ESANN. Volume 89, 89--94.","journal-title":"ESANN."},{"key":"e_1_3_2_2_21_1","unstructured":"Hansheng Ren et al. 2019. Time-series anomaly detection service at Microsoft. In ACM SIGKDD 3009--3017."},{"key":"e_1_3_2_2_22_1","volume-title":"Statistical Evaluation of Anomaly Detectors for Sequences. In ACM SIGKDD Workshop on Mining and Learning from Time Series (KDD MiLeTS).","author":"Scharw\u00e4chter Erik","year":"2020","unstructured":"Erik Scharw\u00e4chter and Emmanuel M\u00fcller. 2020. Statistical Evaluation of Anomaly Detectors for Sequences. In ACM SIGKDD Workshop on Mining and Learning from Time Series (KDD MiLeTS)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Bernhard Sch\u00f6lkopf et al. 2001. Estimating the support of a high-dimensional distribution. Neural computation 13 7 1443--1471.","DOI":"10.1162\/089976601750264965"},{"key":"e_1_3_2_2_24_1","volume-title":"USENIX Workshop on Cyber Security Experimentation and Test.","author":"Hyeok-Ki","unstructured":"Hyeok-Ki Shin et al. 2020. HAI 1.0: HIL-based augmented ICS security dataset. In USENIX Workshop on Cyber Security Experimentation and Test."},{"key":"e_1_3_2_2_25_1","volume-title":"Demystifying Numenta anomaly benchmark","author":"Singh Nidhi","unstructured":"Nidhi Singh and Craig Olinsky. 2017. Demystifying Numenta anomaly benchmark. In IEEE IJCNN."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"Ya Su et al. 2019. Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In ACM SIGKDD 2828--2837.","DOI":"10.1145\/3292500.3330672"},{"key":"e_1_3_2_2_27_1","unstructured":"Nesime Tatbul et al. 2018. Precision and recall for time series. NeurIPS."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2019.107299"},{"key":"e_1_3_2_2_29_1","first-page":"237","article-title":"Assumption-free anomaly detection in time series","volume":"5","author":"Li Wei","year":"2005","unstructured":"Li Wei et al. 2005. Assumption-free anomaly detection in time series. In SSDBM. Volume 5, 237--242.","journal-title":"SSDBM."},{"key":"e_1_3_2_2_30_1","volume-title":"Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress","author":"Wu Renjie","unstructured":"Renjie Wu and Eamonn Keogh. 2021. Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress. IEEE TKDE."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Haowen Xu et al. 2018. Unsupervised anomaly detection via variational autoencoder for seasonal KPIs in web applications. In WWW 187--196","DOI":"10.1145\/3178876.3185996"}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539339","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539339","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:47Z","timestamp":1750186967000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":31,"alternative-id":["10.1145\/3534678.3539339","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539339","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}