{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:56:44Z","timestamp":1753894604453,"version":"3.41.2"},"reference-count":27,"publisher":"Ubiquity Press, Ltd.","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,5,7]]},"DOI":"10.5334\/dsj-2019-016","type":"journal-article","created":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T15:28:01Z","timestamp":1557242881000},"source":"Crossref","is-referenced-by-count":6,"title":["Time Series Prediction Model of Grey Wolf Optimized Echo State Network"],"prefix":"10.5334","volume":"18","author":[{"given":"Huiqing","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingying","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2311-3422","authenticated-orcid":false,"given":"Chun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhirong","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3285","reference":[{"issue":"12","key":"key20190507081330_B1","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.1109\/TNNLS.2015.2404823","article-title":"Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction","volume":"26","year":"2015","journal-title":"IEEE Transactions on Neural Networks & Learning Systems"},{"issue":"3","key":"key20190507081330_B2","first-page":"1","article-title":"A new accuracy measure based on bounded relative error for time series forecasting","volume":"12","year":"2017","journal-title":"Plos One"},{"key":"key20190507081330_B3","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.asoc.2017.01.049","article-title":"PSO-based analysis of Echo State Network parameters for time series forecasting","volume":"55","year":"2017","journal-title":"Applied Soft Computing"},{"issue":"2","key":"key20190507081330_B4","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11063-014-9342-0","article-title":"Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting","volume":"41","year":"2015","journal-title":"Neural Processing Letters"},{"issue":"10","key":"key20190507081330_B5","first-page":"1469","article-title":"LM algorithm in echo state network for chaotic time series prediction","volume":"26","year":"2011","journal-title":"Control & Decision"},{"issue":"1","key":"key20190507081330_B6","article-title":"SVM and SVM ensembles in breast cancer prediction","volume":"12","year":"2017","journal-title":"Plos One"},{"issue":"1","key":"key20190507081330_B7","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/TLA.2017.7827918","article-title":"Research in financial time series forecasting with SVM: Contributions from literature","volume":"15","year":"2017","journal-title":"IEEE Latin America Transactions"},{"key":"key20190507081330_B8","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.asoc.2017.01.043","article-title":"A novel high-order weighted fuzzy time series model and its application in nonlinear time series prediction","volume":"55","year":"2017","journal-title":"Applied Soft Computing"},{"issue":"5","key":"key20190507081330_B9","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1109\/TNNLS.2012.2188414","article-title":"Chaotic time series prediction based on a novel robust echo state network","volume":"23","year":"2012","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"key20190507081330_B10","doi-asserted-by":"crossref","first-page":"142","DOI":"10.13031\/aea.11157","article-title":"A Digester Temperature Prediction Model Based on the Elman Neural Network","volume":"33","year":"2017","journal-title":"Applied Engineering in Agriculture"},{"first-page":"1867","volume-title":"Thirtieth AAAI Conference on Artificial Intelligence","year":"2016","key":"key20190507081330_B11"},{"issue":"1","key":"key20190507081330_B12","first-page":"58","article-title":"A novel model of leaky integrator echo state network for time-series prediction","volume":"159","year":"2015","journal-title":"Neurocomputing"},{"key":"key20190507081330_B13","first-page":"54","article-title":"A hybrid model based on support vector regression and modified harmony search algorithm in time series prediction","year":"2017","journal-title":"2017 5th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). IEEE"},{"issue":"1","key":"key20190507081330_B14","first-page":"587","article-title":"A hybrid wavelet kernel SVM-based method using artificial bee colony algorithm for predicting the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain)","volume":"309","year":"2017","journal-title":"Journal of Computational & Applied Mathematics"},{"issue":"4","key":"key20190507081330_B15","first-page":"463","article-title":"Prediction of BOD based on PSO-ESN neural network","volume":"23","year":"2016","journal-title":"Control Engineering"},{"key":"key20190507081330_B16","first-page":"2627","article-title":"A dual-stage attention-based recurrent neural network for time series prediction","year":"2017","journal-title":"International Joint Conferences on Artificial Intelligence Organization"},{"issue":"2","key":"key20190507081330_B17","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1049\/iet-rpg.2015.0065","article-title":"Temperature prediction of the molten salt collector tube using BP neural network","volume":"10","year":"2016","journal-title":"IET Renewable Power Generation"},{"first-page":"81","volume-title":"Advanced Optimization by Nature-Inspired Algorithms","year":"2018","key":"key20190507081330_B18"},{"issue":"5","key":"key20190507081330_B19","first-page":"175","article-title":"Time Series Analysis and Forecasting","volume":"43","year":"2016","journal-title":"Contributions to Statistics"},{"year":"2007","key":"key20190507081330_B20","article-title":"Water Inflow Forecasting using the Echo State Network: a Brazilian Case Study"},{"issue":"5","key":"key20190507081330_B21","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1007\/s00521-014-1806-7","article-title":"Evolutionary population dynamics and grey wolf optimizer","volume":"26","year":"2015","journal-title":"Neural Computing and Applications"},{"issue":"8","key":"key20190507081330_B22","first-page":"1265","article-title":"RBF neural network time series forecasting based on hybrid evolutionary algorithm","volume":"27","year":"2012","journal-title":"Control & Decision"},{"key":"key20190507081330_B23","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.ijleo.2017.01.073","article-title":"Time series prediction using dynamic Bayesian network","volume":"135","year":"2017","journal-title":"Optik International Journal for Light and Electron Optics"},{"key":"key20190507081330_B24","first-page":"1","article-title":"Non-tuned machine learning approach for hydrological time series forecasting","year":"2016","journal-title":"Neural Computing & Applications"},{"issue":"4","key":"key20190507081330_B25","first-page":"29","article-title":"The combined prediction model based on time series ARIMA and BP neural network","volume":"3","year":"2016","journal-title":"Statistics and Decision"},{"first-page":"5004","article-title":"Speech recognition with prediction-adaptation-correction recurrent neural networks","year":"2015","key":"key20190507081330_B26"},{"key":"key20190507081330_B27","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.neucom.2017.01.053","article-title":"Genetic algorithm optimized double-reservoir echo state network for multi-regime time series prediction","volume":"238","year":"2017","journal-title":"Neurocomputing"}],"container-title":["Data Science Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/doi.org\/10.5334\/dsj-2019-016","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T09:19:55Z","timestamp":1657531195000},"score":1,"resource":{"primary":{"URL":"http:\/\/datascience.codata.org\/articles\/10.5334\/dsj-2019-016\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":27,"alternative-id":["10.5334\/dsj-2019-016"],"URL":"https:\/\/doi.org\/10.5334\/dsj-2019-016","relation":{},"ISSN":["1683-1470"],"issn-type":[{"type":"electronic","value":"1683-1470"}],"subject":[],"published":{"date-parts":[[2019]]},"article-number":"16"}}