{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T12:42:18Z","timestamp":1785156138905,"version":"3.55.0"},"reference-count":41,"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":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002790","name":"Ciena","doi-asserted-by":"publisher","award":["CRDPJ 461084"],"award-info":[{"award-number":["CRDPJ 461084"]}],"id":[{"id":"10.13039\/501100002790","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Canada Research Chair, Tier 1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3026337","type":"journal-article","created":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T20:30:09Z","timestamp":1600979409000},"page":"176540-176554","source":"Crossref","is-referenced-by-count":19,"title":["Gaussian Process Regression Ensemble Model for Network Traffic Prediction"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7243-4594","authenticated-orcid":false,"given":"Abdolkhalegh","family":"Bayati","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9354-7544","authenticated-orcid":false,"given":"Kim-Khoa","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5246-7265","authenticated-orcid":false,"given":"Mohamed","family":"Cheriet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","year":"0"},{"key":"ref38","year":"2020","journal-title":"The CAIDA UCSD Anonymized Internet Traces 2010-2015"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6226-1"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"ho","year":"1998","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2304474"},{"key":"ref37","article-title":"Introduction to time series analysis and forecasting","author":"montgomery","year":"2015","journal-title":"Wiley Series in Probability and Statistics"},{"key":"ref36","first-page":"881","article-title":"Infinite mixtures of Gaussian process experts","author":"rasmussen","year":"2001","journal-title":"Proc 14th Int Conf Neural Inf Process Syst Natural Synth"},{"key":"ref35","first-page":"35","author":"ferreira","year":"2012","journal-title":"Boosting Algorithms A Review of Methods Theory and Applications"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2006.10.009"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1109\/TNNLS.2012.2198074","article-title":"Toward automatic time-series forecasting using neural networks","volume":"23","author":"yan","year":"2012","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref40","year":"2011","journal-title":"Waikato VIII"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582798"},{"key":"ref12","author":"rasmussen","year":"2006","journal-title":"Gaussian Processes for Machine Learning"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2016.7841857"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2018.2875747"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2018.2829773"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2018.2829513"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2018.2796095"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2016.2588500"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2015.2458892"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2246578"},{"key":"ref4","first-page":"1","author":"polikar","year":"2012","journal-title":"Ensemble Machine Learning Methods and Applications"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2953728"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2009.2035983"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2015.2427156"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052585"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2806975"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.156"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2526675"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2014.1035"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2015.2401038"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1364\/JOCN.9.000A35"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1364\/JOCN.10.000D52"},{"key":"ref22","first-page":"891","article-title":"Traffic prediction using FARIMA models","author":"shu","year":"1999","journal-title":"Proc IEEE Int Conf Commun"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/35.601746"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.trit.2016.11.004"},{"key":"ref41","first-page":"373","article-title":"Chapter: Long-range dependence and data network traffic","author":"willinger","year":"2003","journal-title":"Long Range Dependence Theory and Applications"},{"key":"ref23","first-page":"802","article-title":"Convolutional lstm network: A machine learning approach for precipitation nowcasting","author":"xingjian","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3071178.3071321"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2093575"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09204965.pdf?arnumber=9204965","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:09Z","timestamp":1639770969000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9204965\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3026337","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}