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, c and sigma, 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.