The Integrated Methodology of Wavelet Transform and GA based-SVM for Forecasting Share Price
In the analysis of predicting share price based on least squares support vector machine (LS-SVM), the instability of the time series could lead to decrease of prediction accuracy. On the other hand, two SVM parameters, and c _, must be carefully predetermined in establishing an efficient LS-SVM model. In order to solve the problems mentioned above, in this paper, the hybrid of wavelet transform (WT) with GA-SVM model was established. First the chaotic feature of share price is verified with chaos theory. It can be seen that share price possessed chaotic features, providing a basis for performing short-term forecast of share price with the help of chaos theory. Average Mutual Information (AMI) method is used to find the optimal time lag. Then the time series is decomposed by wavelet transform to eliminate the instability. Genetic optimization algorithm (GA) is employed to determine the three parameters of SVM. The effectiveness of proposed model was tested on the prediction of share price of one listed company in China.
Jianguo Zhou Tao Bai Aiguang Zhang Jiming Tian
School of Business Administration North China Electric Power University Baoding, Hebei Province, China
国际会议
2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)
张家界
英文
729-733
2008-06-20（万方平台首次上网日期，不代表论文的发表时间）