基于粒子群优化的最小二乘支持向量机在时间序列预测中的应用

摘要/Abstract

摘要：

为提高基于最小二乘支持向量机(LSSVM)的时间序列预测方法的泛化能力与预测精度,研究了一种基于粒子群优化(PSO)的LSSVM。该方法以交叉验证误差为评价准则,利用PSO对多个具有不同超参数的LSSVM进行基于迭代进化的优化选择,并以交叉验证误差最小的LSSVM作为最终优化后的LSSVM。时间序列预测实例表明,经PSO优化后的LSSVM的预测精度高于未经优化的LSSVM与传统时间序列预测方法的预测精度。

关键词:

最小二乘支持向量机, 粒子群优化, 交叉验证, 时间序列预测

Abstract:

In order to improve the generalization performance and prediction accuracy of LSSVM based time series prediction, a PSO based LSSVM was studied. Firstly, a certain number of LSSVMs were trained by using training samples and then cross-validation error was applied to evaluate the generalization performance of the LSSVMs. Finally, PSO was applied to search for the optimal LSSVM with the smallest cross-validation error. Experiments on time series prediction indicate that LSSVM optimized by PSO has better prediction performance than that not optimized and conventional prediction methods.

Key words:

least square support vector machine(LSSVM), particle swarm optimization(PSO), cross-validation, time series prediction

中图分类号:

TP273

张弦, 王宏力.

基于粒子群优化的最小二乘支持向量机在时间序列预测中的应用

[J]. 中国机械工程, 2011, 22(21): 2572-2576.

ZHANG Xian, WANG Hong-Li.

LSSVM Based on PSO and Its Applications to Time Series Prediction

[J]. China Mechanical Engineering, 2011, 22(21): 2572-2576.

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