期刊
COMPUTERS & MATHEMATICS WITH APPLICATIONS
卷 61, 期 8, 页码 1975-1978出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.camwa.2010.08.041
关键词
Hydrological time series; Prediction; Embedding dimension; Number of nearest neighbors; Bayesian analysis
The embedding dimension and the number of nearest neighbors are very important parameters in the prediction of chaotic time series. To reduce the prediction errors and the uncertainties in the determination of the above parameters, a new chaos Bayesian optimal prediction method (CBOPM) is proposed by choosing optimal parameters in the local linear prediction method (LLPM) and improving the prediction accuracy with Bayesian theory. In the new method, the embedding dimension and the number of nearest neighbors are combined as a parameter set. The optimal parameters are selected by mean relative error (MRE) and correlation coefficient (CC) indices according to optimization criteria. Real hydrological time series are taken to examine the new method. The prediction results indicate that CBOPM can choose the optimal parameters adaptively in the prediction process. Compared with several LLPM models, the CBOPM has higher prediction accuracy in predicting hydrological time series. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.
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