{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T17:51:25Z","timestamp":1774893085360,"version":"3.50.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100013096","name":"Science and Technology Project of State Grid","doi-asserted-by":"publisher","award":["SGJS0000DKJS2000952"],"award-info":[{"award-number":["SGJS0000DKJS2000952"]}],"id":[{"id":"10.13039\/501100013096","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Fundation of Jiangsu Provincial Industry and Information Transformation and Upgrading","award":["Research on key core technologies of artificial intelligence algorithm frameworks, tools and platforms"],"award-info":[{"award-number":["Research on key core technologies of artificial intelligence algorithm frameworks, tools and platforms"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s13042-021-01462-x","type":"journal-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T18:02:27Z","timestamp":1636999347000},"page":"1493-1504","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["TSLOD: a coupled generalized subsequence local outlier detection model for multivariate time series"],"prefix":"10.1007","volume":"13","author":[{"given":"Fan","family":"Meng","sequence":"first","affiliation":[]},{"given":"Yang","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Huihui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hairong","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"1462_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47578-3","volume-title":"Outlier analysis","author":"CC Aggarwal","year":"2017","unstructured":"Aggarwal CC (2017) Outlier analysis. Springer, Berlin"},{"issue":"3","key":"1462_CR2","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","volume":"31","author":"A Bagnall","year":"2017","unstructured":"Bagnall A, Lines J, Bostrom A, Large J, Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Discov 31(3):606\u2013660","journal-title":"Data Min Knowl Discov"},{"key":"1462_CR3","unstructured":"Bagnall AJ, Dau HA, Lines J, Flynn M, Large J, Bostrom A, Southam P, Keogh EJ (2018) The UEA multivariate time series classification archive, 2018. CoRR abs\/1811.0"},{"key":"1462_CR4","unstructured":"Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: KDD workshop 1994, pp 359\u2013370"},{"key":"1462_CR5","doi-asserted-by":"crossref","unstructured":"Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of ACM SIGMOD 2000, pp 1\u201312","DOI":"10.1145\/342009.335388"},{"key":"1462_CR6","doi-asserted-by":"crossref","unstructured":"Bu Y, Leung OTW, Fu AWC, Keogh EJ, Pei J, Meshkin S (2007) WAT: finding top-K discords in time series database. In: Proceedings of ICDM 2007, pp 449\u2013454","DOI":"10.1137\/1.9781611972771.43"},{"issue":"3","key":"1462_CR7","first-page":"15","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv CSUR 41(3):15","journal-title":"ACM Comput Surv CSUR"},{"issue":"5","key":"1462_CR8","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1109\/TKDE.2010.235","volume":"24","author":"V Chandola","year":"2012","unstructured":"Chandola V, Banerjee A, Kumar V (2012) Anomaly detection for discrete sequences: a survey. IEEE Trans Knowl Data Eng 24(5):823\u2013839","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1462_CR9","doi-asserted-by":"crossref","unstructured":"Chau PM, Duc BM, Anh DT (2018) Discord discovery in streaming time series based on an improved HOT SAX algorithm. In: Proceedings of SoICT 2018, ACM, pp 24\u201330","DOI":"10.1145\/3287921.3287929"},{"issue":"1","key":"1462_CR10","doi-asserted-by":"publisher","first-page":"12:1","DOI":"10.1145\/2379776.2379788","volume":"45","author":"P Esling","year":"2012","unstructured":"Esling P, Ag\u00f3n C (2012) Time-series data mining. ACM Comput Surv 45(1):12:1-12:34","journal-title":"ACM Comput Surv"},{"key":"1462_CR11","doi-asserted-by":"crossref","unstructured":"Faloutsos C, Ranganathan M, Manolopoulos Y (1994) Fast subsequence matching in time-series databases. In: Proceedings of ACM SIGMOD, pp 419\u2013429","DOI":"10.1145\/191843.191925"},{"key":"1462_CR12","doi-asserted-by":"crossref","unstructured":"Hyndman RJ, Wang E, Laptev N (2015) Large-scale unusual time series detection. In: 2015 IEEE ICDM workshop, pp 1616\u20131619","DOI":"10.1109\/ICDMW.2015.104"},{"key":"1462_CR13","doi-asserted-by":"publisher","first-page":"1810","DOI":"10.1109\/TKDE.2018.2808532","volume":"30","author":"S Jian","year":"2018","unstructured":"Jian S, Cao L, Lu K, Gao H (2018) Unsupervised coupled metric similarity for non-IID categorical data. IEEE Trans Knowl Data Eng 30:1810\u201323","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1462_CR14","doi-asserted-by":"crossref","unstructured":"Keogh EJ, Lin J, Fu AWC (2005) HOT SAX: efficiently finding the most unusual time series subsequence. In: Proceedings of ICDM 2005, pp 226\u2013233","DOI":"10.1109\/ICDM.2005.79"},{"key":"1462_CR15","doi-asserted-by":"crossref","unstructured":"Keogh EJ, Rakthanmanon T (2013) Fast Shapelets: a scalable algorithm for discovering time series shapelets. In: Proceedings of ICDM 2013, pp 668\u2013676","DOI":"10.1137\/1.9781611972832.74"},{"key":"1462_CR16","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.knosys.2013.09.015","volume":"54","author":"G Li","year":"2013","unstructured":"Li G, Br\u00e4ysy O, Jiang L, Wu Z, Wang Y (2013) Finding time series discord based on bit representation clustering. Knowl Based Syst 54:243\u2013254","journal-title":"Knowl Based Syst"},{"key":"1462_CR17","doi-asserted-by":"crossref","unstructured":"Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD workshop, ACM, pp 2\u201311","DOI":"10.1145\/882082.882086"},{"issue":"3","key":"1462_CR18","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s10994-008-5093-3","volume":"74","author":"U Rebbapragada","year":"2009","unstructured":"Rebbapragada U, Protopapas P, Brodley CE, Alcock CR (2009) Finding anomalous periodic time series. Mach Learn 74(3):281\u2013313","journal-title":"Mach Learn"},{"key":"1462_CR19","doi-asserted-by":"crossref","unstructured":"Schlegl T, Seeb\u00f6ck P, Waldstein SM, Schmidt-Erfurth U, Langs G (2017) Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International conference on information processing in medical imaging, Springer, pp 146\u2013157","DOI":"10.1007\/978-3-319-59050-9_12"},{"issue":"1","key":"1462_CR20","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/s10618-012-0300-z","volume":"28","author":"E Schubert","year":"2014","unstructured":"Schubert E, Zimek A, Kriegel HP (2014) Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min Knowl Discov 28(1):190\u2013237","journal-title":"Data Min Knowl Discov"},{"key":"1462_CR21","doi-asserted-by":"crossref","unstructured":"Su Y, Zhao Y, Niu C, Liu R, Sun W, Pei D (2019) Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2828\u20132837","DOI":"10.1145\/3292500.3330672"},{"key":"1462_CR22","doi-asserted-by":"crossref","unstructured":"Vlachos M, Gunopulos D, Kollios G (2002) discovering similar multidimensional trajectories. In: Proceedings of ICDE 2002, pp 673\u2013684","DOI":"10.1109\/ICDE.2002.994784"},{"issue":"1","key":"1462_CR23","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1145\/321796.321811","volume":"21","author":"RA Wagner","year":"1974","unstructured":"Wagner RA, Fischer MJ (1974) The string-to-string correction problem. J ACM 21(1):168\u2013173","journal-title":"J ACM"},{"key":"1462_CR24","doi-asserted-by":"crossref","unstructured":"Ye L, Keogh EJ (2009) Time series shapelets: a new primitive for data mining. In: Proceedings of SIGKDD 2009, pp 947\u2013956","DOI":"10.1145\/1557019.1557122"},{"key":"1462_CR25","doi-asserted-by":"crossref","unstructured":"Zhang C, Song D, Chen Y, Feng X, Lumezanu C, Cheng W, Ni J, Zong B, Chen H, Chawla NV (2019) A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In: Proceedings of the AAAI conference on artificial intelligence, vol\u00a033, pp 1409\u20131416","DOI":"10.1609\/aaai.v33i01.33011409"},{"key":"1462_CR26","doi-asserted-by":"crossref","unstructured":"Zhou C, Paffenroth RC (2017) Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 665\u2013674","DOI":"10.1145\/3097983.3098052"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01462-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-021-01462-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01462-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T08:51:58Z","timestamp":1649321518000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-021-01462-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,15]]},"references-count":26,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["1462"],"URL":"https:\/\/doi.org\/10.1007\/s13042-021-01462-x","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,15]]},"assertion":[{"value":"21 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}