TITLE:
Based on Multiple Scales Forecasting Stock Price with a Hybrid Forecasting System
AUTHORS:
Yuqiao Li, Xiaobei Li, Hongfang Wang
KEYWORDS:
Hybrid Forecasting System, Stock Price Forecast, Wavelet Transform, Autoregressive Moving Average Models, Kalman Filter, Back Propagation Neural Network
JOURNAL NAME:
American Journal of Industrial and Business Management,
Vol.6 No.11,
November
29,
2016
ABSTRACT: 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 28th June 2011 to 24th 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.