An integrated chaotic time series prediction model based on efficient extreme learning machine and differential evolution
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Tao Xu | Wei Guo | Zonglei Lu
[1] L. Cao. Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .
[2] H. S. Kim,et al. Nonlinear dynamics , delay times , and embedding windows , 1999 .
[3] F. Clette,et al. The Sidc: World Data Center for the Sunspot Index , 2004 .
[4] Rob J Hyndman,et al. 25 years of time series forecasting , 2006 .
[5] D. Rand,et al. Dynamical Systems and Turbulence, Warwick 1980 , 1981 .
[6] Atin Das,et al. Chaotic analysis of the foreign exchange rates , 2007, Appl. Math. Comput..
[7] Nitin Muttil,et al. Monthly flow forecast for Mississippi River basin using artificial neural networks , 2013, Neural Computing and Applications.
[8] Saeed Zolfaghari,et al. Chaotic time series prediction with residual analysis method using hybrid Elman-NARX neural networks , 2010, Neurocomputing.
[9] Héctor Pomares,et al. Soft-computing techniques and ARMA model for time series prediction , 2008, Neurocomputing.
[10] Chin-Teng Lin,et al. A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[11] Vahid Majazi Dalfard,et al. Efficiency appraisal and ranking of decision-making units using data envelopment analysis in fuzzy environment: a case study of Tehran stock exchange , 2012, Neural Computing and Applications.
[12] Peter L. Bartlett,et al. The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.
[13] Urban Bilstrup,et al. Chaotic time series prediction using brain emotional learning-based recurrent fuzzy system (BELRFS) , 2013, Int. J. Reason. based Intell. Syst..
[14] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[15] Andrew M. Fraser,et al. Information and entropy in strange attractors , 1989, IEEE Trans. Inf. Theory.
[16] Ying Mei,et al. Chaotic Bayesian optimal prediction method and its application in hydrological time series , 2011, Comput. Math. Appl..
[17] Stephen A. Vavasis,et al. Complete Orthogonal Decomposition for Weighted Least Squares , 1995 .
[18] Mengjie Zhang,et al. Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction , 2012, Neurocomputing.
[19] Wenting Han,et al. Prediction of multivariate chaotic time series via radial basis function neural network , 2013, Complex..
[20] Ramin Ayanzadeh,et al. Discrete Time Dynamic Neural Networks for Predicting Chaotic Time Series , 2014 .
[21] Schwartz,et al. Singular-value decomposition and the Grassberger-Procaccia algorithm. , 1988, Physical review. A, General physics.
[22] Jiao Jianjun. Parameters determination based on combinative evolutionary algorithm for reconstructing phase-space in chaos time series , 2013 .
[23] Qi Wu,et al. The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine , 2010, Expert Syst. Appl..
[24] D. Kugiumtzis. State space reconstruction parameters in the analysis of chaotic time series—the role of the time window length , 1996, comp-gas/9602002.
[25] Hui Wang,et al. Short-term wind power prediction using differential EMD and relevance vector machine , 2013, Neural Computing and Applications.
[26] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[27] Gene H. Golub,et al. Matrix computations , 1983 .
[28] Saeed Zolfaghari,et al. TAGUCHI'S DESIGN OF EXPERIMENT IN COMBINATION SELECTION FOR A CHAOTIC TIME SERIES FORECASTING METHOD USING ENSEMBLE ARTIFICIAL NEURAL NETWORKS , 2013, Cybern. Syst..
[29] Qi-Lun Zheng,et al. Chaotic Time Series Prediction Based on Evolving Recurrent Neural Networks , 2007, 2007 International Conference on Machine Learning and Cybernetics.
[30] Xiaodong Li,et al. Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm , 2011, Neural Computing and Applications.
[31] Babak Nadjar Araabi,et al. Predicting Chaotic Time Series Using Neural and Neurofuzzy Models: A Comparative Study , 2006, Neural Processing Letters.
[32] Sunil S. Bhagwat,et al. Prediction of Melting Points of Organic Compounds Using Extreme Learning Machines , 2008 .
[33] Yong Yu,et al. Sales forecasting using extreme learning machine with applications in fashion retailing , 2008, Decis. Support Syst..
[34] F. Takens. Detecting strange attractors in turbulence , 1981 .
[35] Rahib Hidayat Abiyev,et al. Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction , 2011, Neural Computing and Applications.
[36] Arash Miranian,et al. Developing a Local Least-Squares Support Vector Machines-Based Neuro-Fuzzy Model for Nonlinear and Chaotic Time Series Prediction , 2013, IEEE Transactions on Neural Networks and Learning Systems.
[37] Oscar Castillo,et al. An Interval Type-2 Fuzzy Neural Network for Chaotic Time Series Prediction with Cross-Validation and Akaike Test , 2011, Soft Computing for Intelligent Control and Mobile Robotics.
[38] Witold Pedrycz,et al. Soft Computing for Intelligent Control and Mobile Robotics , 2011, Soft Computing for Intelligent Control and Mobile Robotics.
[39] Saeed Zolfaghari,et al. RESIDUAL ANALYSIS AND COMBINATION OF EMBEDDING THEOREM AND ARTIFICIAL INTELLIGENCE IN CHAOTIC TIME SERIES FORECASTING , 2011, Appl. Artif. Intell..
[40] Xiaolong Wang,et al. A support vector machine based MSM model for financial short-term volatility forecasting , 2011, Neural Computing and Applications.
[41] Baher Abdulhai,et al. Short-term traffic flow forecasting : parametric and nonparametric approaches via emotional temporal difference learning , 2013 .