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Nonlinear Time Series Prediction Using Improved Least Squares Support Vector Machine
Xu Ruirui *,Bian Guoxing
Department of Physics, Nankai University
*Correspondence author
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Funding: none
Opened online:13 January 2005
Accepted by: none
Citation: Xu Ruirui,Bian Guoxing.Nonlinear Time Series Prediction Using Improved Least Squares Support Vector Machine [OL]. [13 January 2005] http://en.paper.edu.cn/en_releasepaper/content/1459
 
 
In the research of the nonlinear time series prediction, we apply the least squares support vector machine (LS-SVM) to use. In order to improve the precision LS-SVM has been discussed in the following aspects. First, the parameterγand multi-step prediction capabilities of the LS-SVM network are investigated. Then we employ clustering method in the model to prune the number of the support values. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.
Keywords:least squares support vector machine, nonlinear time series, prediction, clustering
 
 
 

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