{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T04:05:02Z","timestamp":1774929902030,"version":"3.50.1"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Key Research and Development Plan Project of Shandong province, China","award":["2017CXGC0614"],"award-info":[{"award-number":["2017CXGC0614"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.2988797","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T00:14:58Z","timestamp":1587428098000},"page":"74933-74942","source":"Crossref","is-referenced-by-count":26,"title":["A Time Convolutional Network Based Outlier Detection for Multidimensional Time Series in Cyber-Physical-Social Systems"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7435-4774","authenticated-orcid":false,"given":"Chao","family":"Meng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9339-8745","authenticated-orcid":false,"given":"Xue Song","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2214-0576","authenticated-orcid":false,"given":"Xiu Mei","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7983-3683","authenticated-orcid":false,"given":"Tao","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/222"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57529-2_62"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/29.21701"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2015.141"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2016.7795584"},{"key":"ref30","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"bai","year":"2018","journal-title":"arXiv 1803 01271"},{"key":"ref37","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref36","first-page":"901","article-title":"Weight normalization: A simple reparameterization to accelerate training of deep neural networks","author":"salimans","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn (ICML-10)"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref10","first-page":"139","article-title":"One-class SVMs for document classification","volume":"2","author":"manevitz","year":"2001","journal-title":"J Mach Learn Res"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15825-4_10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2013.12.018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2466557"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2016.2610839"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s12206-008-0603-6"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2018.00029"},{"key":"ref17","article-title":"Feedforward neural network for time series anomaly detection","author":"rong","year":"2018","journal-title":"arXiv 1812 08389"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489605"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2018.04.005"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.accinf.2016.04.001"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011409"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.5120\/19004-0502"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-018-0494-9"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700360"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2967768"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2771237"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2950192"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1700303"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870151"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2019.05.016"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.2307\/2289995"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2921096"},{"key":"ref42","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2015.11.066"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3330139"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2017.03.019"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3323926"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1177\/0142331210397571"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09072131.pdf?arnumber=9072131","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:56:29Z","timestamp":1642002989000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9072131\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2988797","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}