AJIBM  Vol.6 No.11 , November 2016
Based on Multiple Scales Forecasting Stock Price with a Hybrid Forecasting System
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.
Cite this paper: 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.

[1]   Armano, G., Marchesi, M. and Murru, A. (2005) A Hybrid Genetic-Neural Architecture for Stock Indexes Forecasting. Information Sciences, 170, 3-33.

[2]   Shumway, R.H. and Stoffer, D.S. (2014) Time Series Analysis and Its Applications. Springer-Verlag Inc., New York, 57.

[3]   Franses, P.H. and Ghijsels, H. (1999) Additive Outliers GARCH and Forecasting Volatility. International Journal of Forecasting, 15, 1-9.

[4]   Sarantis, N. (2001) Nonlinearities, Cyclical Behavior and Predictability in Stock Markets: International Evidence. International Journal of Forecating, 17, 459-482.

[5]   Konar, A. (2005) Computational Intelligence: Principles, Techniques. Springer, Berlin.

[6]   Khashei, M., Bijaria, M. and Ardali, G.A. (2009) Improvement of Auto-Regressive Integrated Moving Average Models Using Fuzzy Logic and Artificial Neural Networks (ANNs). Neurocomputing, 72, 956-967.

[7]   Hansen, J.V. and Nelson, R.D. (2002) Data Mining of Time Series Using Stacked Generalizes. Neurocomputing, 43, 173-184.

[8]   Ture, M. and Kurt, I. (2006) Comparison of Four Different Time Series Methods to Forecast Hepatitis A Virus Infection. Expert Systems with Applications, 31, 41-46.

[9]   Thawornwong, S. and Enke, D. (2004) The Adaptive Selection of Financial and Economic Variables for Use with Artificial Neural Networks. Neurocomputing, 56, 205-232.

[10]   Peters, E.E. (1996) Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. John Wiley & Sons, New York.

[11]   Gencay, R., Selcuk, F. and Whitcher, B. (2001) Wavelets and Other Filtering Methods in Finance and Economics. Academic Press, New York.

[12]   Burrus, C.S., Gopinath, R.A. and Guo, H. (1998) Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice Hall, New Jersey.

[13]   Bruggemann, R. (2004) Model Reduction Methods for Vector Autoregressive Processes. Springer-Verlag, Berlin Heidelberg.

[14]   Kalman, R.E. (1960) A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82, 35-45.

[15]   Brown, R.G. (1983) Introduction to Random Signal Analysis and Kalman Filtering. Wiley, New York.

[16]   Haykin, S. (1999) Neural Networks: A Comprehensive Foundation. Prentice-Hall, New Jersey.

[17]   Enke, D. and Thawornwong, S. (2005) The Use of Data Mining and Neural Networks for Forecasting Stock Market Returns. Expert Systems with Applications, 29, 927-940.