Parameter selection in time series prediction based on nu-support vector regression

查看全文 作　　者：[1]胡亮;[1]Che;[1]Xilong 机构地区：[1]College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China 出　　处：《High Technology Letters》索引2009年第15卷第4期,共6页 基　　金：Supported by the National Natural Science Foundation of China (No. 60873235&60473099);the Science-Technology Development Key Project of Jilin Province of China (No. 20080318);the Program of New Century Excellent Talents in University of China (No. NCET-06-0300). 摘　　要：The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure. 关 键 词：时间序列预测 支持向量回归 参数选择 基础 预测模型 预测误差 优化性能 支持向量机