Master Thesis PCA - Fuzzy - SVR Stock Price Prediction
نویسنده
چکیده
This study provides a principal component analysis-fuzzy-support vector regression model for stock price prediction. Stocks with similar historical trends are selected using principal component analysis. Fuzzy information granulation is performed to construct a probability density for stock prices. Support vector regression is implemented to generate a regression function for future price prediction. This method suits for any sample size with any noise distribution type and eliminates the complicated fine tuning process compared with other Neural Network procedures. Besides, the use of fuzzy information granulation extends the prediction output form from a point to an interval with a certain probability assigned.
منابع مشابه
Forecasting Stock Market Using Wavelet Transforms and Neural Networks: An integrated system based on Fuzzy Genetic algorithm (Case study of price index of Tehran Stock Exchange)
The jamor purpose of the present research is to predict the total stock market index of Tehran Stock Exchange, using a combined method of Wavelet transforms, Fuzzy genetics, and neural network in order to predict the active participations of finance market as well as macro decision makers.To do so, first the prediction was made by neural network, then a series of price index was decomposed by w...
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