{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:57:47Z","timestamp":1771703867463,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2015,4,11]],"date-time":"2015-04-11T00:00:00Z","timestamp":1428710400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2016,5]]},"DOI":"10.1007\/s00521-015-1903-2","type":"journal-article","created":{"date-parts":[[2015,4,10]],"date-time":"2015-04-10T03:47:00Z","timestamp":1428637620000},"page":"883-898","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["An integrated chaotic time series prediction model based on efficient extreme learning machine and differential evolution"],"prefix":"10.1007","volume":"27","author":[{"given":"Wei","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zonglei","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,4,11]]},"reference":[{"issue":"1","key":"1903_CR1","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.amc.2006.06.106","volume":"185","author":"A Das","year":"2007","unstructured":"Das A, Das P (2007) Chaotic analysis of the foreign exchange rates. Appl Math Comput 185(1):388\u2013396","journal-title":"Appl Math Comput"},{"issue":"3","key":"1903_CR2","first-page":"1","volume":"23","author":"Y Bao","year":"2013","unstructured":"Bao Y, Wang H, Wang BN (2013) Short-term wind power prediction using differential EMD and relevance vector machine. Neural Comput Appl 23(3):1\u20137","journal-title":"Neural Comput Appl"},{"issue":"1","key":"1903_CR3","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s00521-012-0977-3","volume":"23","author":"J Abdi","year":"2013","unstructured":"Abdi J, Moshiri B, Abdulhai B, Sedigh AK (2013) Short-term traffic flow forecasting: parametric and nonparametric approaches via emotional temporal difference learning. Neural Comput Appl 23(1):141\u2013159","journal-title":"Neural Comput Appl"},{"issue":"7","key":"1903_CR4","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1007\/s00521-013-1419-6","volume":"24","author":"C Sivapragasam","year":"2014","unstructured":"Sivapragasam C, Vanitha S, Muttil N, Suganya K, Suji S, Selvi MT, Selvi R, Sudha SJ (2014) Monthly flow forecast for Mississippi River basin using artificial neural networks. Neural Comput Appl 24(7):1785\u20131793","journal-title":"Neural Comput Appl"},{"issue":"8","key":"1903_CR5","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1016\/j.camwa.2010.08.041","volume":"61","author":"XH Yang","year":"2011","unstructured":"Yang XH, Mei Y, She DX, Li JQ (2011) Chaotic Bayesian optimal prediction method and its application in hydrological time series. Comput Math Appl 61(8):1975\u20131978","journal-title":"Comput Math Appl"},{"issue":"3","key":"1903_CR6","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s11063-006-9021-x","volume":"24","author":"A Gholipour","year":"2006","unstructured":"Gholipour A, Araabi BN, Lucas C (2006) Predicting chaotic time series using neural and neurofuzzy models: a comparative study. Neural Process Lett 24(3):217\u2013239","journal-title":"Neural Process Lett"},{"issue":"3","key":"1903_CR7","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.ijforecast.2006.01.001","volume":"22","author":"JG Gooijer De","year":"2006","unstructured":"De Gooijer JG, Hyndman RJ (2006) 25\u00a0years of time series forecasting. Int J Forecast 22(3):443\u2013473","journal-title":"Int J Forecast"},{"key":"1903_CR8","doi-asserted-by":"crossref","unstructured":"Ma QL, Zheng QL, Peng H, Zhong TW, Xu LQ (2007) Chaotic time series prediction based on evolving recurrent neural networks. In: 2007 International conference on machine learning and cybernetics, Hong Kong, 2007. IEEE, pp 3496\u20133500","DOI":"10.1109\/ICMLC.2007.4370752"},{"issue":"1","key":"1903_CR9","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TSMCC.2008.2002333","volume":"39","author":"CJ Lin","year":"2009","unstructured":"Lin CJ, Chen CH, Lin CT (2009) A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications. IEEE Trans Syst Man Cybern Part C Appl Rev 39(1):55\u201368","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"issue":"13","key":"1903_CR10","doi-asserted-by":"crossref","first-page":"2540","DOI":"10.1016\/j.neucom.2010.06.004","volume":"73","author":"M Ardalani-Farsa","year":"2010","unstructured":"Ardalani-Farsa M, Zolfaghari S (2010) Chaotic time series prediction with residual analysis method using hybrid Elman-NARX neural networks. Neurocomputing 73(13):2540\u20132553","journal-title":"Neurocomputing"},{"key":"1903_CR11","unstructured":"Castro JR, Castillo O, Melin P, Mendoza O, Rodr\u00edguez-D\u00edaz A (2011) An interval type-2 fuzzy neural network for chaotic time series prediction with cross-validation and Akaike test. In: Castillo O, Kacprzyk J, Pedrycz W (eds) Soft computing for intelligent control and mobile robotics. Berlin Heidelberg, Springer, pp 269\u2013285"},{"issue":"1","key":"1903_CR12","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.neucom.2012.01.014","volume":"86","author":"R Chandra","year":"2012","unstructured":"Chandra R, Zhang MJ (2012) Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing 86(1):116\u2013123","journal-title":"Neurocomputing"},{"issue":"4","key":"1903_CR13","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1080\/01969722.2013.789653","volume":"44","author":"M Ardalani-Farsa","year":"2013","unstructured":"Ardalani-Farsa M, Zolfaghari S (2013) Taguchi\u2019s design of experiment in combination selection for a chaotic time series forecasting method using ensemble artificial neural networks. Cybern Syst 44(4):351\u2013377","journal-title":"Cybern Syst"},{"issue":"4","key":"1903_CR14","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1002\/cplx.21441","volume":"18","author":"DY Chen","year":"2013","unstructured":"Chen DY, Han WT (2013) Prediction of multivariate chaotic time series via radial basis function neural network. Complexity 18(4):55\u201366","journal-title":"Complexity"},{"issue":"1","key":"1903_CR15","doi-asserted-by":"crossref","first-page":"24","DOI":"10.3923\/jai.2014.24.34","volume":"7","author":"F Marzban","year":"2014","unstructured":"Marzban F, Ayanzadeh R, Marzban P (2014) Discrete time dynamic neural networks for predicting chaotic time series. J Artif Intell 7(1):24\u201334","journal-title":"J Artif Intell"},{"issue":"2","key":"1903_CR16","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s00521-010-0414-4","volume":"20","author":"RH Abiyev","year":"2011","unstructured":"Abiyev RH (2011) Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction. Neural Comput Appl 20(2):249\u2013259","journal-title":"Neural Comput Appl"},{"issue":"2","key":"1903_CR17","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/TNNLS.2012.2227148","volume":"24","author":"A Miranian","year":"2013","unstructured":"Miranian A, Abdollahzade M (2013) Developing a local least-squares support vector machines-based neuro-fuzzy model for nonlinear and chaotic time series prediction. IEEE Trans Neural Netw Learn Syst 24(2):207\u2013218","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"1903_CR18","doi-asserted-by":"crossref","first-page":"1776","DOI":"10.1016\/j.eswa.2009.07.054","volume":"37","author":"Q Wu","year":"2010","unstructured":"Wu Q (2010) The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine. Expert Syst Appl 37(2):1776\u20131783","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1903_CR19","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s00521-011-0742-z","volume":"22","author":"B Wang","year":"2013","unstructured":"Wang B, Huang H, Wang X (2013) A support vector machine based MSM model for financial short-term volatility forecasting. Neural Comput Appl 22(1):21\u201328","journal-title":"Neural Comput Appl"},{"issue":"1","key":"1903_CR20","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s00521-011-0741-0","volume":"22","author":"JP Donate","year":"2013","unstructured":"Donate JP, Li X, S\u00e1nchez GG, de Miguel AS (2013) Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm. Neural Comput Appl 22(1):11\u201320","journal-title":"Neural Comput Appl"},{"issue":"1","key":"1903_CR21","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489\u2013501","journal-title":"Neurocomputing"},{"issue":"1","key":"1903_CR22","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.dss.2008.07.009","volume":"46","author":"ZL Sun","year":"2008","unstructured":"Sun ZL, Choi TM, Au KF, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46(1):411\u2013419","journal-title":"Decis Support Syst"},{"issue":"3","key":"1903_CR23","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1021\/ie0704647","volume":"47","author":"AU Bhat","year":"2008","unstructured":"Bhat AU, Merchant SS, Bhagwat SS (2008) Prediction of melting points of organic compounds using extreme learning machines. Ind Eng Chem Res 47(3):920\u2013925","journal-title":"Ind Eng Chem Res"},{"issue":"2","key":"1903_CR24","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1137\/S089547989528079X","volume":"18","author":"PD Hough","year":"1997","unstructured":"Hough PD, Vavasis SA (1997) Complete orthogonal decomposition for weighted least squares. SIAM J Matrix Anal Appl 18(2):369\u2013392","journal-title":"SIAM J Matrix Anal Appl"},{"issue":"22","key":"1903_CR25","first-page":"1","volume":"61","author":"WZ Zhang","year":"2012","unstructured":"Zhang WZ, Long W, Jiao JJ (2012) Parameters determination based on composite evolutionary algorithm for reconstructing phase-space in chaos time series. Acta Phys Sin 61(22):1\u20137","journal-title":"Acta Phys Sin"},{"key":"1903_CR26","doi-asserted-by":"crossref","unstructured":"Takens F (1981) Detecting strange attractors in turbulence. In: Rand D, Young L-S (eds) Dynamical systems and turbulence, Warwick 1980. Berlin Heidelberg, Springer, pp 366\u2013381","DOI":"10.1007\/BFb0091924"},{"issue":"6","key":"1903_CR27","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1103\/PhysRevA.38.3017","volume":"38","author":"AM Albano","year":"1988","unstructured":"Albano AM, Muench J, Schwartz C, Mees A, Rapp P (1988) Singular-value decomposition and the Grassberger\u2013Procaccia algorithm. Phys Rev A 38(6):3017\u20133026","journal-title":"Phys Rev A"},{"issue":"2","key":"1903_CR28","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1109\/18.32121","volume":"35","author":"AM Fraser","year":"1989","unstructured":"Fraser AM (1989) Information and entropy in strange attractors. IEEE Trans Inf Theory 35(2):245\u2013262","journal-title":"IEEE Trans Inf Theory"},{"issue":"1","key":"1903_CR29","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/0167-2789(96)00054-1","volume":"95","author":"D Kugiumtzis","year":"1996","unstructured":"Kugiumtzis D (1996) State space reconstruction parameters in the analysis of chaotic time series\u2014the role of the time window length. Physica D 95(1):13\u201328","journal-title":"Physica D"},{"issue":"1","key":"1903_CR30","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0167-2789(97)00118-8","volume":"110","author":"L Cao","year":"1997","unstructured":"Cao L (1997) Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110(1):43\u201350","journal-title":"Physica D"},{"issue":"1","key":"1903_CR31","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/S0167-2789(98)00240-1","volume":"127","author":"HS Kim","year":"1999","unstructured":"Kim HS, Eykholt R, Salas J (1999) Nonlinear dynamics, delay times, and embedding windows. Physica D 127(1):48\u201360","journal-title":"Physica D"},{"key":"1903_CR32","volume-title":"Matrix computations","author":"GH Golub","year":"2012","unstructured":"Golub GH, Van Loan CF (2012) Matrix computations, vol 3. JHU Press, Baltimore"},{"issue":"2","key":"1903_CR33","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/18.661502","volume":"44","author":"PL Bartlett","year":"1998","unstructured":"Bartlett PL (1998) The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans Inf Theory 44(2):525\u2013536","journal-title":"IEEE Trans Inf Theory"},{"issue":"4","key":"1903_CR34","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution\u2014a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341\u2013359","journal-title":"J Global Optim"},{"key":"1903_CR35","unstructured":"SIDC (World Data Center for the Sunspot Index) (2014). http:\/\/sidc.oma.be\/"},{"issue":"4","key":"1903_CR36","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.neucom.2007.07.018","volume":"71","author":"I Rojas","year":"2008","unstructured":"Rojas I, Valenzuela O, Rojas F, Guill\u00e9n A, Herrera LJ, Pomares H, Marquez L, Pasadas M (2008) Soft-computing techniques and ARMA model for time series prediction. Neurocomputing 71(4):519\u2013537","journal-title":"Neurocomputing"},{"issue":"1","key":"1903_CR37","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/08839514.2011.529263","volume":"25","author":"M Ardalani-Farsa","year":"2011","unstructured":"Ardalani-Farsa M, Zolfaghari S (2011) Residual analysis and combination of embedding theorem and artificial intelligence in chaotic time series forecasting. Appl Artif Intell 25(1):45\u201373","journal-title":"Appl Artif Intell"},{"issue":"2","key":"1903_CR38","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1504\/IJRIS.2013.057273","volume":"5","author":"M Parsapoor","year":"2013","unstructured":"Parsapoor M, Bilstrup U (2013) Chaotic time series prediction using brain emotional learning-based recurrent fuzzy system (BELRFS). Int J Reason Based Intell Syst 5(2):113\u2013126","journal-title":"Int J Reason Based Intell Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-1903-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-015-1903-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-1903-2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T09:15:10Z","timestamp":1717838110000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-015-1903-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,4,11]]},"references-count":38,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2016,5]]}},"alternative-id":["1903"],"URL":"https:\/\/doi.org\/10.1007\/s00521-015-1903-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,4,11]]}}}