关键词: 支持向量机, 金融时间序列, 混合核函数

Abstract: Kernel function in Support Vector Machine（SVM） has a great influence on the quality of model.Currently，in financial time series forecasting，Radial Basis Function（RBF） kernel is primary kernel function，next is polynomial kernel function.However，Every kernel has its advantages and disadvantages.Combining two or more kernels is one of efficient method for improving the ability of learning and generalization.In this paper，RBF and polynomial kernel have been combined to forecast the financial time series.The experiment result shows that the strategy of mixing the kernel function can obtain better performance in financial time series forecasting.

Key words: Support Vector Machine（SVM）, financial time series, mixture kernels