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.
The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variation for prediction. In order to avoid prediction performance degradation caused by improper parameters, the method of parallel multidimensional step search (PMSS) is proposed for users to select best parameters in training support vector machine to get a prediction model. A series of tests are performed to evaluate the modeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendency of time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is also employed to verify the optimization performance of PMSS algorithm and comparative results indicate that training error can take the minimum over the interval around planar data point corresponding to selected parameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.
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).
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
parameter selection, time series prediction, nu-support vector regression (Nu-SVR), parallel multidimensional step search (PMSS)