{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T23:29:31Z","timestamp":1768260571771,"version":"3.49.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation: NSF","doi-asserted-by":"crossref","award":["2103976"],"award-info":[{"award-number":["2103976"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation: NSF","doi-asserted-by":"crossref","award":["OIA-1757207"],"award-info":[{"award-number":["OIA-1757207"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation: NSF","doi-asserted-by":"crossref","award":["CNS-2008910"],"award-info":[{"award-number":["CNS-2008910"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation: NSF","doi-asserted-by":"crossref","award":["RI-2104537"],"award-info":[{"award-number":["RI-2104537"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001665","name":"French National Research Agency","doi-asserted-by":"crossref","award":["ANR-19-P3IA-0002"],"award-info":[{"award-number":["ANR-19-P3IA-0002"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s10618-022-00911-7","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T11:07:08Z","timestamp":1673435228000},"page":"627-669","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["DAMP: accurate time series anomaly detection on trillions of datapoints and ultra-fast arriving data streams"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4812-9658","authenticated-orcid":false,"given":"Yue","family":"Lu","sequence":"first","affiliation":[]},{"given":"Renjie","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Abdullah","family":"Mueen","sequence":"additional","affiliation":[]},{"given":"Maria A.","family":"Zuluaga","sequence":"additional","affiliation":[]},{"given":"Eamonn","family":"Keogh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"911_CR1","unstructured":"Aubet F-X, Z\u00fcgner D, Gasthaus J (2021) Monte Carlo EM for deep time series anomaly detection. arXiv:2112.14436 [cs, stat]"},{"key":"911_CR2","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-030-91445-5_12","volume-title":"Advanced analytics and learning on temporal data","author":"J Audibert","year":"2021","unstructured":"Audibert J, Marti S, Guyard F, Zuluaga MA (2021) From univariate to multivariate time series anomaly detection with non-local information. In: Lemaire V, Malinowski S, Bagnall A et al (eds) Advanced analytics and learning on temporal data. Springer International Publishing, Cham, pp 186\u2013194"},{"key":"911_CR4","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/s00778-021-00655-8","volume":"30","author":"P Boniol","year":"2021","unstructured":"Boniol P, Linardi M, Roncallo F et al (2021a) Unsupervised and scalable subsequence anomaly detection in large data series. VLDB J 30:909\u2013931. https:\/\/doi.org\/10.1007\/s00778-021-00655-8","journal-title":"VLDB J"},{"key":"911_CR5","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.14778\/3467861.3467863","volume":"14","author":"P Boniol","year":"2021","unstructured":"Boniol P, Paparrizos J, Palpanas T, Franklin MJ (2021b) SAND: streaming subsequence anomaly detection. Proc VLDB Endow 14:1717\u20131729","journal-title":"Proc VLDB Endow"},{"key":"911_CR7","unstructured":"Case Western Reserve University Bearing Data Center (2021) Available: https:\/\/csegroups.case.edu\/bearingdatacenter\/home. Accessed: Nov. 15, 2021"},{"key":"911_CR8","unstructured":"CNC Crashes. Video. (15 Feb 2018). from https:\/\/youtu.be\/t2tBtZCa7j4?t=205. Retrieved December 20, 2021"},{"key":"911_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2022.105040","volume":"161","author":"A Daigavane","year":"2022","unstructured":"Daigavane A, Wagstaff KL, Doran G et al (2022) Unsupervised detection of Saturn magnetic field boundary crossings from plasma spectrometer data. Comput Geosci 161:105040","journal-title":"Comput Geosci"},{"key":"911_CR10","unstructured":"DAMP (2022) https:\/\/sites.google.com\/view\/discord-aware-matrix-profile"},{"key":"911_CR11","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1109\/JAS.2019.1911747","volume":"6","author":"HA Dau","year":"2019","unstructured":"Dau HA, Bagnall A, Kamgar K et al (2019) The UCR time series archive. IEEE\/CAA J Autom Sin 6:1293\u20131305. https:\/\/doi.org\/10.1109\/JAS.2019.1911747","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"911_CR12","unstructured":"Doshi K, Abudalou S, Yilmaz Y (2022) TiSAT: time series anomaly transformer. arXiv:2203.05167 [cs, eess, stat]"},{"key":"911_CR13","unstructured":"Higham NJ (2002) Accuracy and stability of numerical algorithms, 2 edn. ISBN: 978-0-89871-521-7"},{"key":"911_CR14","doi-asserted-by":"crossref","unstructured":"Hundman K, Constantinou V, Laporte C et al (2018) Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. ACM, London United Kingdom, pp 387\u2013395","DOI":"10.1145\/3219819.3219845"},{"key":"911_CR15","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.1007\/s10618-020-00702-y","volume":"34","author":"S Imani","year":"2020","unstructured":"Imani S, Madrid F, Ding W et al (2020) Introducing time series snippets: a new primitive for summarizing long time series. Data Min Knowl Disc 34:1713\u20131743. https:\/\/doi.org\/10.1007\/s10618-020-00702-y","journal-title":"Data Min Knowl Disc"},{"key":"911_CR16","unstructured":"Keogh E (2021) Irrational exuberance why we should not believe 95% of papers on time series anomaly detection. In: 7th SIGKDD workshop on mining and learning from time series at SIGKDD 2021. Workshop Keynote https:\/\/www.youtube.com\/watch?v=Vg1p3DouX8w&t=324s"},{"key":"911_CR17","unstructured":"Khansa HE, Gervet C and Brouillet A (2012) Prominent discord discovery with matrix profile: application to climate data insight. In: 10th international conference of advanced computer science & information technology (ACSIT 2022) May 21~22, 2022, Zurich, Switzerland"},{"key":"911_CR18","doi-asserted-by":"publisher","first-page":"93","DOI":"10.52964\/AMJA.0553","volume":"11","author":"R Kirti","year":"2012","unstructured":"Kirti R, Karadi R (2012) Cardiac tamponade: atypical presentations after cardiac surgery. Acute Med 11:93\u201396","journal-title":"Acute Med"},{"key":"911_CR19","unstructured":"Mueen A, Zhu Y, Yeh M et al (2017) The fastest similarity search algorithm for time series subsequences under euclidean distance. http:\/\/www.cs.unm.edu\/~mueen\/FastestSimilaritySearch.htmlAccessed 24 Janurary, 2022"},{"key":"911_CR21","doi-asserted-by":"crossref","unstructured":"Nakamura T, Imamura M, Mercer R, Keogh E (2020) Merlin: parameter-free discovery of arbitrary length anomalies in massive time series archives. In: 2020 IEEE international conference on data mining (ICDM). IEEE, Sorrento, Italy, pp 1190\u20131195","DOI":"10.1109\/ICDM50108.2020.00147"},{"key":"911_CR22","unstructured":"National Weather Service. January 24, 2019 heavy rain and flooding. From https:\/\/www.weather.gov\/aly\/24Jan19HeavyRainFlood. Retrieved May 1 2022"},{"key":"911_CR23","doi-asserted-by":"publisher","first-page":"93155","DOI":"10.1109\/ACCESS.2020.2990528","volume":"8","author":"D Neupane","year":"2020","unstructured":"Neupane D, Seok J (2020) Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review. IEEE Access 8:93155\u201393178. https:\/\/doi.org\/10.1109\/ACCESS.2020.2990528","journal-title":"IEEE Access"},{"key":"911_CR24","unstructured":"Nilsson F (2022) Joint human-machine exploration of industrial time series using the matrix profile. In: Halmstad university, school of information technology, Halmstad embedded and intelligent systems research (EIS), CAISR\u2014center for applied intelligent systems research"},{"key":"911_CR25","unstructured":"Palpanas T (2022) Personal communication June 4th 2022"},{"key":"911_CR26","doi-asserted-by":"crossref","unstructured":"Paparrizos J, Kang Y, Boniol P et al (2022) TSB-UAD: An end-to-end benchmark suite for univariate time-series anomaly detection. In: Proceedings of the VLDB endowment (PVLDB) journal","DOI":"10.14778\/3529337.3529354"},{"key":"911_CR27","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1109\/LRA.2018.2801475","volume":"3","author":"D Park","year":"2018","unstructured":"Park D, Hoshi Y, Kemp CC (2018) A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder. IEEE Robot Autom Lett 3:1544\u20131551. https:\/\/doi.org\/10.1109\/LRA.2018.2801475","journal-title":"IEEE Robot Autom Lett"},{"key":"911_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2020.109892","volume":"215","author":"JY Park","year":"2020","unstructured":"Park JY, Wilson E, Parker A, Nagy Z (2020) The good, the bad, and the ugly: data-driven load profile discord identification in a large building portfolio. Energy Build 215:109892","journal-title":"Energy Build"},{"key":"911_CR29","unstructured":"Silive.com. Wild storm pelts Staten Island with giant hail\u2014\u2018threat of tornado has passed\u2019 from https:\/\/www.silive.com\/news\/2019\/05\/nws-issues-tornado-warning-for-staten-island.html. Retrieved May 1 2022"},{"key":"911_CR30","doi-asserted-by":"crossref","unstructured":"Su Y, Zhao Y, Niu C et al (2019) Robust anomaly detection for multivariate time series through stochastic recurrent neural network pp 2828\u20132837","DOI":"10.1145\/3292500.3330672"},{"key":"911_CR31","first-page":"161","volume-title":"Time series encodings with temporal convolutional networks","author":"M Thill","year":"2020","unstructured":"Thill M, Konen W, B\u00e4ck T (2020) Time series encodings with temporal convolutional networks. Springer, Cham, pp 161\u2013173"},{"key":"911_CR32","doi-asserted-by":"publisher","first-page":"103692","DOI":"10.1016\/j.compind.2022.103692","volume":"140","author":"HT Truong","year":"2022","unstructured":"Truong HT, Ta BP, Le QA et al (2022) Light-weight federated learning-based anomaly detection for time-series data in industrial control systems. Comput Ind 140:103692. https:\/\/doi.org\/10.1016\/j.compind.2022.103692","journal-title":"Comput Ind"},{"key":"911_CR33","unstructured":"Wastewater News. Valentine\u2019s day storm slams California, pushing water agencies to the edge. From www.news.cornell.edu\/Chronicle\/00\/5.18.00\/wireless_class.html. Retrieved Dec 1 2021"},{"key":"911_CR34","unstructured":"Wikipedia. Leap year problem. from https:\/\/en.wikipedia.org\/wiki\/Leap_year_problem. Retrieved December 1, 2021"},{"key":"911_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3112126","author":"R Wu","year":"2021","unstructured":"Wu R, Keogh E (2021) Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress. IEEE Trans Knowl Data Eng. https:\/\/doi.org\/10.1109\/TKDE.2021.3112126","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"911_CR36","doi-asserted-by":"crossref","unstructured":"Yeh C-CM, Zheng Y, Wang J et al (2021) Error-bounded approximate time series joins using compact dictionary representations of time series. CoRR abs arXiv:2112.12965","DOI":"10.1137\/1.9781611977172.21"},{"key":"911_CR37","doi-asserted-by":"crossref","unstructured":"Yeh C-CM, Zhu Y, Dau HA et al (2019) Online amnestic dtw to allow real-time golden batch monitoring. pp 2604\u20132612","DOI":"10.1145\/3292500.3330650"},{"key":"911_CR38","doi-asserted-by":"crossref","unstructured":"Zheng X, Xu N, Trinh L et al (2021) PSML: a multi-scale time-series dataset for machine learning in decarbonized energy grids. arXiv preprint arXiv: 2110.06324","DOI":"10.1038\/s41597-022-01455-7"},{"key":"911_CR39","doi-asserted-by":"crossref","unstructured":"Zhu Y, Yeh C-CM, Zimmerman Z et al (2018) Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds. In: IEEE pp 837\u2013846","DOI":"10.1109\/ICDM.2018.00099"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00911-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-022-00911-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00911-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T11:34:44Z","timestamp":1677238484000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-022-00911-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,11]]},"references-count":36,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["911"],"URL":"https:\/\/doi.org\/10.1007\/s10618-022-00911-7","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,11]]},"assertion":[{"value":"18 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research complies with all ethical guidelines at the three institutions represented.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"No human subjects were used by the current authors. The ECG data was collected by others over a decade ago, under strict human subject protocols.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human or animal rights"}}]}}