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The Research On Wavelet Network Prediction Model And Its Application In Stock Market Forecasting

Posted on:2005-07-11Degree:DoctorType:Dissertation
Country:ChinaCandidate:S P LvFull Text:PDF
GTID:1116360125470672Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
Stock market is a hotspot that investors, administrators and economist pay attention to. Many academicians have focused on the research of stock market forecasting model since 19B.C. since the stock market was established. The linear statistical forecasting models, such as AR and ARIMA, have been applied in this area widely, but have not had ideal effect. In recently years, many academicians have regarded stock market as a nonlinear deterministic kinetic system. Using great the rules of nonlinear deterministic system to study the stock price shows more and more vitality. Along with the development of nonlinear theory and artificial intelligence, wavelet analysis and wavelet network become cogent tools for money market analysis and forecasting.This paper does deeply research on the wavelet network and establishes a short term prediction model which serves the time series analysis of stock price. The main research is the application and realization of the wavelet network prediction. The main work is as the following:From the configuration theory of wavelet network, this paper deeply analyses the three typical structures widely used now. Considering the factors such as the network algorithm, approaching ability and the numerous information in frequency domain, this paper points out the disadvantages of the models based RBF-WNN (wavelet network with scale function as energizing function) and MLP-WNN (wavelet network with wavelet function as energizing function), brings up a multi-resolution analysis of wavelet network (MRA-WNN) so as to realize the nonlinear time series prediction of stock price. Using MRA-WNN, we can approach the whole developing trend of the stock market (the contour), and also capture the changing details.Using the method of phase space reconstruction, we get the state vector andregard it as the multidimensional input of MRA-WNN. Then, this text establishes multidimensional prediction MRA-WNN model, and apples it on the prediction of stock price time series for the first time. Otherwise, it gives a realization method. Based on MRA-WNN, this paper brings up an algorithm of BP combined with multi-resolution analysis, which resolves the problems that are uncertain note numbers of the hidden layer for traditional training algorithm and are difficult to study complex time series by the single-scale algorithm of BP network. Taking Shenzheng's integrated index for example, this paper forecasts the stock price time series using the MRA-WNN and RBF-WNN model with the same structure respectively. The simulation result indicates that the MAR-WNN has a high prediction precision.On the other hand, this paper does research on the combination of wavelet analysis and neural network and brings up a neural network prediction method and its concrete realization process based wavelet decomposition and reconstruction. Through this method, this paper decomposed the price function into a series of wavelets in different frequency range, whose fluctuation rule can be easily grasped. This method increases the neural network prediction precision, and makes it possible to predict signals with different characteristics with prediction models of different parameters. The prediction of Shenzheng's integrative index indicates that this method is more accurate than the single neural network prediction model which directly used the series of price fluctuation to predict, and can be widely used in some nonlinear time series prediction.
Keywords/Search Tags:Multi-resolution Analysis, Neural Network, Wavelet Network, Multi-resolution Analysis of Wavelet Network, Phase Space Reconstruction, Stock Market Forecasting
PDF Full Text Request
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