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Study on Stock Time Series Based on Rough Set and RBF Network

Author: WangTianE
Tutor: YeDeQian
School: Qingdao Technological University
Course: Applied Computer Technology
Keywords: Time Series Rough Set RBF network Stock trend Stock inflection point Decision Support Model
CLC: TP183
Type: Master's thesis
Year: 2009
Downloads: 62
Quote: 1
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Traditional time series analysis methods to deal with simple linear problem and achieved good results , but the stock complex nonlinear systems appeared to be inadequate . The emergence of artificial neural networks to nonlinear time series analysis provides a new effective way , but there are still some drawbacks . Problems encountered by the neural network stock prediction , the subject will be rough set theory introduced predictive models and conduct in-depth research . (1 ) the use of rough set theory pretreatment stock data extracted from a large number of raw data in the core knowledge and to improve the efficiency of the analysis . Conditional information entropy discretization method of calculation is very complex , and the choice of the subject of the original algorithm of discrete points and calculation process improvement, be an inspiration shows information entropy discretization method , and the stock data discretization . (2) the genetic algorithm is applied to a minimum reduction of looking for the weaknesses of the basic genetic algorithm , to improve its genetic . Stock data reduction using attribute reduction method based on improved genetic algorithm , so as to get better reduction results . ( 3) the radial basis function (RBF) neural network learning algorithms have their own advantages and disadvantages, in practical applications , it is difficult to determine which algorithm is more ideal . The subject through experiments , analyze and compare the performance of three commonly used algorithm of RBF network , neural network model to determine stock prediction . ( 4) The proposed definition and classification of stock inflection point ( three types of inflection point ) , set the inflection point parameters corresponding decision support model , select the appropriate level of stock data , predict stock trend inflection point , the actual operation for small and medium investors to provide decision support . Finally , through a large number of experiments to prove the accuracy of the stock time series analysis method based on rough sets and RBF network integration , as well as the effectiveness solve complex network structure , slow learning .

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Artificial Neural Networks and Computing
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