{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:03:24Z","timestamp":1781885004941,"version":"3.54.5"},"reference-count":36,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation, Singapore, through the AI Singapore Program","doi-asserted-by":"publisher","award":["AISG2-RP-2020-019"],"award-info":[{"award-number":["AISG2-RP-2020-019"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Singapore Institute of Technology through the Ignition Grant","award":["R-MOE-E103-F006"],"award-info":[{"award-number":["R-MOE-E103-F006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Wireless Commun."],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1109\/twc.2022.3216004","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T01:16:35Z","timestamp":1667524595000},"page":"3114-3127","source":"Crossref","is-referenced-by-count":20,"title":["Unsupervised TCN-AE-Based Outlier Detection for Time Series With Seasonality and Trend for Cellular Networks"],"prefix":"10.1109","volume":"22","author":[{"given":"Ronghong","family":"Mo","sequence":"first","affiliation":[{"name":"Singapore Institute of Technology, 10 Dover Dr, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9915-8697","authenticated-orcid":false,"given":"Yiyang","family":"Pei","sequence":"additional","affiliation":[{"name":"Singapore Institute of Technology, 10 Dover Dr, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4458-1180","authenticated-orcid":false,"given":"Neelakantam V.","family":"Venkatarayalu","sequence":"additional","affiliation":[{"name":"Singapore Institute of Technology, 10 Dover Dr, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pereira","family":"Nathaniel Joseph","sequence":"additional","affiliation":[{"name":"MSI-Global, Blk 3 Level 1, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1861-9760","authenticated-orcid":false,"given":"A. Benjamin","family":"Premkumar","sequence":"additional","affiliation":[{"name":"Singapore Institute of Technology, 10 Dover Dr, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1701-8122","authenticated-orcid":false,"given":"Sumei","family":"Sun","sequence":"additional","affiliation":[{"name":"Singapore Institute of Technology, 10 Dover Dr, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon Kok Kan","family":"Foo","sequence":"additional","affiliation":[{"name":"M1 Ltd., 10 International Business Park, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107751"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/sym11111350"},{"key":"ref34","article-title":"Temporal convolutional networks for action segmentation and detection","author":"lea","year":"2016","journal-title":"arXiv 1611 05267"},{"key":"ref12","article-title":"Automatic anomaly detection in the cloud via statistical learning","author":"hochenbaum","year":"2017","journal-title":"arXiv 1704 07706"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICNIDC.2018.8525714"},{"key":"ref36","year":"2015","journal-title":"Webscope S5 Dataset Ydata-Labeled-Time-Series-Anomalies-V1_0"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2886457"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2973023"},{"key":"ref30","article-title":"Time series anomaly detection using convolutional neural networks and transfer learning","author":"wen","year":"2019","journal-title":"arXiv 1905 13628"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-65070-5"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s12544-015-0170-8"},{"key":"ref32","first-page":"1","article-title":"Anomaly detection based on convolutional recurrent autoencoder for IoT time series","volume":"52","author":"yin","year":"2020","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1213\/4\/042050"},{"key":"ref2","year":"2017","journal-title":"Cisco Visual Networking Index Global Mobile Data Traffic Forecast Update"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/APWCS50173.2021.9548759"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1177\/1475921717691260"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2988797"},{"key":"ref19","article-title":"Semi-supervised variational temporal convolutional network for IoT communication multi-anomaly detection","author":"xu","year":"2021","journal-title":"arXiv 2104 01813"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1177\/1475921720934051"},{"key":"ref24","article-title":"Developing an unsupervised real-time anomaly detection scheme for time series with multi-seasonality","author":"kumar","year":"2021","journal-title":"arXiv 1908 01146"},{"key":"ref23","article-title":"Deep learning for anomaly detection: A review","author":"pang","year":"2020","journal-title":"arXiv 2007 02500"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1002\/aic.690370209"},{"key":"ref25","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"bai","year":"2018","journal-title":"arXiv 1803 01271"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1983.10487848"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref21","article-title":"Real-time anomaly detection for advanced manufacturing: Improving on Twitter’s state of the art","author":"ryan","year":"2019","journal-title":"arXiv 1911 05376"},{"key":"ref28","article-title":"Time series anomaly detection; detection of anomalous drops with limited features and sparse examples in noisy highly periodic data","author":"shipmon","year":"2017","journal-title":"arXiv 1708 03665"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2018.00029"},{"key":"ref29","first-page":"89","article-title":"Long short term memory networks for anomaly detection in time series","author":"malhotra","year":"2015","journal-title":"Proc Eur Symp Artif Neural Netw Comput Intell Mach Learn (ESANN)"},{"key":"ref8","first-page":"1","article-title":"RobustTAD: Robust time series anomaly detection via decomposition and convolutional neural networks","author":"gao","year":"2020","journal-title":"Proc KDD Workshop Mining Learn Time (MileTS)"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47578-3"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICTC49870.2020.9289226"},{"key":"ref4","author":"barnett","year":"1994","journal-title":"Outliers in Statistical Data"},{"key":"ref3","author":"hyndman","year":"2018","journal-title":"Forecasting Principles and Practice"},{"key":"ref6","article-title":"Deep learning for anomaly detection: A survey","author":"chalapathy","year":"2019","journal-title":"arXiv 1901 03407"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"}],"container-title":["IEEE Transactions on Wireless Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7693\/10122187\/09933184.pdf?arnumber=9933184","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T17:47:01Z","timestamp":1685382421000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9933184\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":36,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/twc.2022.3216004","relation":{},"ISSN":["1536-1276","1558-2248"],"issn-type":[{"value":"1536-1276","type":"print"},{"value":"1558-2248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5]]}}}