Nonlinear time series modeling and prediction using functional weights wavelet neural network-based state-dependent AR model

Neurocomputing

Volumn 86, Issue , 2012, Pages 59-74

  Inoussa, Garba ^a   Peng, Hui ^a   Wu, Jun ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Autoregression; Functional weights wavelet network; Modeling; Nonlinear time series; Optimization; Prediction; Wavelet networks

Indexed keywords

AR MODELS; AUTO-REGRESSIVE; AUTOREGRESSION; FREQUENCY SPACES; FUNCTIONAL APPROXIMATION; LEARNING APPROACH; LEAST SQUARE METHODS; LINEAR SUBSPACE; MODEL PARAMETERS; NETWORK-BASED; NON-LINEAR PARAMETERS; NONLINEAR TIME SERIES; NONLINEAR TIME SERIES MODELING; OPTIMIZATION PROCESS; PARAMETER SEARCH SPACE; PREDICTION PROBLEM; STATE-DEPENDENT; WAVELET NETWORK; WAVELET NEURAL NETWORKS;

DYNAMICS; FORECASTING; LEAST SQUARES APPROXIMATIONS; MODELS; OPTIMIZATION; REGRESSION ANALYSIS;

TIME SERIES;

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTORGRESSIVE MODEL; COMPUTER PREDICTION; CONTROLLED STUDY; FREQUENCY ANALYSIS; LEAST SQUARE METHOD; LEVEMBERG MARQUARDT METHOD; MATHEMATICAL MODEL; MATHEMATICAL PARAMETERS; PERFORMANCE MEASUREMENT SYSTEM; PRIORITY JOURNAL; PROCESS DESIGN; PROCESS DEVELOPMENT; PROCESS OPTIMIZATION; TIME SERIES ANALYSIS; WAVELET ANALYSIS;

EID: 84862802588     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.01.010     Document Type: Article

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