支持向量机在金融时间序列预测中的应用  

Application of Support Vector Machines in Financial Time Series Forecasting

作　　者：吴萌[1] 徐全智[2] 

机构地区：[1]西南财经大学经济数学系,成都610074 [2]电子科技大学应用数学学院,成都610054

出　　处：《电子科技大学学报》2007年第S1期442-444,共3页Journal of University of Electronic Science and Technology of China

摘　　要：阐述了支持向量机在金融时间序列预测中应用中的理论基础.将支持向量机预测模型应用于上证指数的建模和预测中,并与BP神经网络模型进行了比较.最后得出支持向量机由于采用了新型的结构风险最小化准则表现出良好的推广能力,具有较高的精度,在金融时间序列的预测上要优于BP神经网络模型.Because the financial time series are inherently noisy,non-stationary and deterministicaUy chaotic, it is difficult to describe this system by traditional methods.The authors presented a novel load forecasting method i.e.an improved Support Vector Machines(SVM).The object of this paper is to examine the feasibility of SVM in financial time series forecasting by comparing it with a back-propagation(BP)neural network.Analysis of the experiment results proves that it is advantageous to apply SVMs to forecast fmancial time series.

关 键 词：BP神经网络 金融时间序列 预测 支持向量机 

分 类 号：F830[经济管理—金融学] F224