{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T23:41:22Z","timestamp":1772754082666,"version":"3.50.1"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"name":"Engineering and Technology Research Center of Guangdong Province for Logistics Supply Chain and Internet of Things","award":["GDDST[2016]176"],"award-info":[{"award-number":["GDDST[2016]176"]}]},{"name":"Key Laboratory of Cloud Computing for Super-integration Cloud Computing in Guangdong Province","award":["610245048129"],"award-info":[{"award-number":["610245048129"]}]},{"name":"Engineering and Technology Research Center of Guangdong Province for Big Data Intelligent Processing","award":["GDDST[2013]1513-1-11"],"award-info":[{"award-number":["GDDST[2013]1513-1-11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2900371","type":"journal-article","created":{"date-parts":[[2019,2,21]],"date-time":"2019-02-21T19:56:51Z","timestamp":1550779011000},"page":"26102-26115","source":"Crossref","is-referenced-by-count":86,"title":["An Ensemble Model Based on Adaptive Noise Reducer and Over-Fitting Prevention LSTM for Multivariate Time Series Forecasting"],"prefix":"10.1109","volume":"7","author":[{"given":"Fagui","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8863-2576","authenticated-orcid":false,"given":"Muqing","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Liangming","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yunsheng","family":"Lu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/366"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2008.09.014"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1023\/B:STCO.0000035301.49549.88"},{"key":"ref31","author":"you","year":"2018","journal-title":"Accurate fast and scalable kernel ridge regression on parallel and distributed systems"},{"key":"ref30","first-page":"830","article-title":"Estimating structured vector autoregressive models","author":"melnyk","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref37","first-page":"1833","article-title":"Nonlinear dynamic boltzmann machines for time-series prediction","author":"dasgupta","year":"2017","journal-title":"Proc AAAI"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2011.0550"},{"key":"ref35","first-page":"326","article-title":"Short-term traffic flow online forecasting based on kernel adaptive filter","volume":"9","author":"jun","year":"2018","journal-title":"Meas Sci Instrum"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2017.09.009"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.07.015"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.3390\/app8081286","article-title":"Wavelet decomposition and convolutional LSTM networks based improved deep learning model for solar irradiance forecasting","volume":"8","author":"wang","year":"2018","journal-title":"Appl Sci"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2018.2881606"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ISSPIT.2017.8388665"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/e20050361"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.87.022911"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.06.052"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s11869-018-0585-1"},{"key":"ref17","first-page":"106","article-title":"A robust approach for multivariate time series forecasting","author":"pang","year":"2017","journal-title":"Proc Int Symp Commun Inf Technol"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2016.2558200"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1002\/cem.2912"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1515\/9780691218632"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2017.11.053"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.2307\/2284333"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/RISE.2017.8378214"},{"key":"ref6","author":"cirstea","year":"2018","journal-title":"Correlated time series forecasting using deep neural networks A summary of results"},{"key":"ref29","first-page":"1843","article-title":"Robust estimation of transition matrices in high dimensional heavy-tailed vector autoregressive processes","volume":"37","author":"qiu","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.3390\/app8081286","article-title":"Wavelet decomposition and convolutional LSTM networks based improved deep learning model for solar irradiance forecasting","volume":"8","author":"wang","year":"2018","journal-title":"Appl Sci"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2009.2037773"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.nonrwa.2009.01.004"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.12.003"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0180944"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.12.013"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.03.049"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2016.02.001"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2018.07.070"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.03.002"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.06.161"},{"key":"ref41","author":"chang","year":"2018","journal-title":"A memory-network based solution for multivariate time-series forecasting"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2015.0257"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2018.01.038"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.88.174102"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.18520\/cs\/v115\/i1\/159-165"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08648338.pdf?arnumber=8648338","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T06:17:17Z","timestamp":1643264237000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8648338\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2900371","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}