论文信息 - A Separate-Predict-Superimpose Predicting Model for Stock

A Separate-Predict-Superimpose Predicting Model for Stock

The purpose of this research is to propose a more precise predicting model, the Separate-Predict-Superimpose Model, for time series, especially for the stock price and the stock risk than the established predicting method. In this model, time series are separated into three parts, including trend ingredient, periodic ingredient and random ingredient. Then the different suitable predicting methods are applying to predict different ingredients to receive accurate outcome. Ultimately, the final predicting result is superimposed by the three ingredient predicting outcome. The wavelet analysis, combination predict method, exponent smoothness method, Fourier Transform, fitting analysis and Autoregressive Moving Average (ARMA) are adopted in this model.

[1] David Johnstone,et al. Economic Interpretation of Probabilities Estimated by Maximum Likelihood or Score , 2011, Manag. Sci..

[2] Valeriy V. Gavrishchaka,et al. Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting , 2006, Comput. Manag. Sci..

[3] Shie-Jue Lee,et al. A multiple-kernel support vector regression approach for stock market price forecasting , 2011, Expert Syst. Appl..

[4] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1972 .

[5] V. Ediger,et al. ARIMA forecasting of primary energy demand by fuel in Turkey , 2007 .

[6] Alex YiHou Huang,et al. Asymmetric dynamics of stock price continuation , 2012 .

[7] Jerome H. Friedman. Multivariate adaptive regression splines (with discussion) , 1991 .

[8] Farookh Khadeer Hussain,et al. Support vector regression with chaos-based firefly algorithm for stock market price forecasting , 2013, Appl. Soft Comput..

[9] David C. Yen,et al. Predicting stock returns by classifier ensembles , 2011, Appl. Soft Comput..

[10] J. Friedman. Multivariate adaptive regression splines , 1990 .

[11] Francis Eng Hock Tay,et al. Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.