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http://dx.doi.org/10.5391/JKIIS.2009.19.2.206

Minimized Stock Forecasting Features Selection by Automatic Feature Extraction Method  

Lee, Sang-Hong (Dept. of Computer Software, Kyungwon University)
Lim, Joon-S. (Dept. of Computer Software, Kyungwon University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.19, no.2, 2009 , pp. 206-211 More about this Journal
Abstract
This paper presents a methodology to 1-day-forecast stock index using the automatic feature extraction method based on the neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by automatically removing the worst input features one by one. CPP$_{n,m}$(Current Price Position of the day n: a percentage of the difference between the price of the day n and the moving average from the day n-1 to the day n-m) and the 2 wavelet transformed coefficients from the recent 32 days of CPP$_{n,m}$ are selected as minimized features using bounded sum of weighted fuzzy membership functions (BSWFMs). For the data sets, from 1989 to 1998, the proposed method shows that the forecast rate is 60.93%.
Keywords
Fuzzy Neural Networks; Feature Selection; Wavelet Transforms; Stock;
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Times Cited By KSCI : 1  (Citation Analysis)
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