支持向量机在期货价格预测中的应用研究

Future Price Pediction Based on Support Vector Machine

关于期货价格准确预测是投资者关心的问题。针对影响期货价格因素间存在高度的非线性、影响因子间存在冗余等特征,传统期货价格预测方法无法消除数据之间冗余和捕捉非线性规律导致预测精度较低,为了提高期货价格预测精度,提出一种基于主成份分析(PCA)的支持向量机(SVM)期货价格预测方法(PCA-SVM)。首先利用主成分分析对期货价格影响因子进行分析和处理,消除各因子之间的高度冗余性,通过选择贡献大的主成分有效地除去无关的影响因子,加快支持向量机学习速度,提高预测精度,然后利用支持向量机对保留的主成成分进行建模预测。利用PCA-SVM模型对2009年8月Kcbt期货价格进行了验证性分析和测试,仿真结果表明,预测效果稳定,提高期货价格预测精度。证明PCA-SVM是一种有效、高精度的期货价格预测方法。

Researching about futures＇ price pediction problems.Effecting factors of future price have the features of highly redundant,nonlinear and time-varying.Traditional method cannot eliminate data redundancy,so that the prediction accuracy is very low.In order to improve the accuracy of future price,a new method is proposed based on principal component analysis（PCA） and support vector machine（SVM）.Firstly,principal component analysis,which can eliminate the nonlinearity of variables,is used to accomplish data preprocessing by extracting characteristic information from training data set,then the support vector machine is used to predict the future price data.Future pice data of Kcbt in August in 2009 are tested and verified by PCA-SVM.The test results reveal that PCA-SVM has improved prediction accuracy significantly compared with the reference models.The PCA-SVM algorithm has good performances in reducing the dimension of the input spaces as well as increasing forecasting accuracy.PCA-SVM is a high efficient future pice pediction method.