{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T18:50:46Z","timestamp":1786215046728,"version":"3.56.0"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100011395","name":"Major Science and Technology Program for Water Pollution Control and Treatment of China","doi-asserted-by":"publisher","award":["2018ZX07111005"],"award-info":[{"award-number":["2018ZX07111005"]}],"id":[{"id":"10.13039\/501100011395","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62073005"],"award-info":[{"award-number":["62073005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802015"],"award-info":[{"award-number":["61802015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Automat. Sci. Eng."],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1109\/tase.2021.3077537","type":"journal-article","created":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T15:46:59Z","timestamp":1621612019000},"page":"1869-1879","source":"Crossref","is-referenced-by-count":312,"title":["A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM"],"prefix":"10.1109","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4610-0141","authenticated-orcid":false,"given":"Jing","family":"Bi","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, School of Software Engineering, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, School of Software Engineering, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8475-419X","authenticated-orcid":false,"given":"Haitao","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2148-0923","authenticated-orcid":false,"given":"Jia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Lyle School of Engineering, Southern Methodist University, Dallas, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5408-8752","authenticated-orcid":false,"given":"MengChu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2019.2892480"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2899224"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.32890\/jict2019.18.1.1"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2004.1312898"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ITSIM.2008.4631947"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1080\/17517575.2015.1048833"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ITNEWS.2008.4488164"},{"key":"ref8","first-page":"279","article-title":"Comparison of SVM and LS-SVM for regression","volume-title":"Proc. Int. Conf. Neural Netw. Brain","author":"Wang"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/5635673"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CCIS.2012.6664250"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2020.1003108"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2896887"},{"key":"ref13","article-title":"Recurrent neural networks for time series forecasting","volume-title":"arXiv:1901.00069","author":"Petneh\u00e1zi","year":"2019"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CCIS.2018.8691406"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON.2016.7848593"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"ref17","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","volume-title":"arXiv:1803.01271","author":"Bai","year":"2018"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2935504"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8080876"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICIS46139.2019.8940265"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1021\/ac60214a047"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/26.380206"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/90.282603"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.06.029"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.1927.0007"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1970.10481180"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.5121\/ijcnc.2012.4409"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICSPCC.2013.6663896"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.1997.605367"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3991\/ijim.v3i1.284"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/BigDataCongress.2016.63"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3195106.3195117"},{"key":"ref33","first-page":"594","article-title":"Prediction of Internet traffic using time series and neural networks","volume-title":"Proc. Int. Work-Conf. Time Anal.","author":"Katris"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0394.2010.00568.x"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1057\/palgrave.jors.2601589"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.05.052"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2016.7844673"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.03.014"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2017.11.054"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCI.2017.8126198"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2016.0208"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC.2018.8581000"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ISIE.2017.8001465"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2017.0-110"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911723"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2009.2037773"},{"issue":"1","key":"ref48","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":"ref49","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v022.i08"},{"key":"ref50","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref52","volume-title":"Technical Analysis From A to Z","author":"Achelis","year":"2001"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.3354\/cr030079"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2017915732"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.4236\/jsip.2012.31006"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2948658"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/IDAP.2017.8090299"},{"key":"ref58","article-title":"Activation functions: Comparison of trends in practice and research for deep learning","volume-title":"arXiv:1811.03378","author":"Nwankpa","year":"2018"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2955567"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2020.1003300"}],"container-title":["IEEE Transactions on Automation Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8856\/9814439\/09439149.pdf?arnumber=9439149","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:24:35Z","timestamp":1704846275000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9439149\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":60,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tase.2021.3077537","relation":{},"ISSN":["1545-5955","1558-3783"],"issn-type":[{"value":"1545-5955","type":"print"},{"value":"1558-3783","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7]]}}}