Chaotic Bayesian optimal prediction method and its application in hydrological time series

Authors:
Xiao-Hua Yang;Ying Mei;Dun-Xian She;Jian-Qiang Li
Affiliations:
The Key Laboratory of Water and Sediment, Ministry of Education, State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China;The Key Laboratory of Water and Sediment, Ministry of Education, State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China;The Key Laboratory of Water and Sediment, Ministry of Education, State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China;Water Resources and Hydropower Planning and Design General Institute, MWR, Beijing 100011, China
Venue:
Computers & Mathematics with Applications
Year:
2011

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Abstract

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.