Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

XU Rui-Rui; BIAN Guo-Xing; GAO Chen-Feng; CHEN Tian-Lun

【期刊名称】《《理论物理通讯（英文版）》》 【年(卷),期】2005(043)006

【摘要】The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction.First, the parameter γ and multi-step prediction capabilities of the LS-SVM network are discussed. 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. 【总页数】5页(1056-1060)

【关键词】least squares support vector machine; nonlinear time series; prediction; clustering

【作者】XU Rui-Rui; BIAN Guo-Xing; GAO Chen-Feng; CHEN Tian-Lun 【作者单位】Department of Physics Nankai University Tianjin 300071 China

【正文语种】中文 【中图分类】O4 【相关文献】

1.Discussion of Some Problems About Nonlinear Time Series Prediction Using v-Support Vector Machine [J], GAO Cheng-Feng; CHEN Tian-Lun;

NAN Tian-Shi

2.Chaotic time series prediction using least squares support vector machines [J], Ye Mei-Ying; Wang Xiao-Dong

3.Time series online prediction algorithm based on least squares support vector machine [J], WU Qiong; LIU Wen-ying; YANG Yi-han 4.Residuals-Based Deep Least Square Support Vector Machine with Redundancy Test Based Model Selection to Predict Time Series [J], Yanhua Yu; Jie Li

5.Local Prediction of Chaotic Time Series Based on Support Vector Machine [J], LI Heng-Chao; ZHANG Jia-Shu

以上内容为文献基本信息，获取文献全文请下载