Author(s)

Yuqiao Li¹, Xiaobei Li², Hongfang Wang³

¹School of Government, Beijing Normal University, Beijing, China.
²College of International Cultural Exchange, Hainan University, Hainan, China.
³Unicom Information Navigation Co. Ltd., Beijing, China.

This paper presents an integration prediction method which is called a hybrid forecasting system based on multiple scales. In this method, the original data are decomposed into multiple layers by the wavelet transform and the multiple layers are divided into low-frequency, intermediate-frequency and high-frequency signal layers. Then autoregressive moving average models, Kalman filters and Back Propagation neural network models are employed respectively for predicting the future value of low-frequency, intermediate-frequency and high-frequency signal layers. An effective algorithm for predicting the stock prices is developed. The price data with the Shandong Gold Group of Shanghai stock exchange market from 28^th June 2011 to 24^th June 2012 are used to illustrate the application of the hybrid forecasting system based on multiple scales in predicting stock price. The result shows that time series forecasting can be produced by forecasting on low-frequency, intermediate-frequency and high-frequency signal layers separately. The actual value and the forecasting results are matching exactly. Therefore, the forecasting result of simulation experiments is excellent.

Hybrid Forecasting System, Stock Price Forecast, Wavelet Transform, Autoregressive Moving Average Models, Kalman Filter, Back Propagation Neural Network

Li, Y. , Li, X. and Wang, H. (2016) Based on Multiple Scales Forecasting Stock Price with a Hybrid Forecasting System. American Journal of Industrial and Business Management, 6, 1102-1112. doi: 10.4236/ajibm.2016.611103.