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http://dx.doi.org/10.5392/JKCA.2011.11.6.001

Forecasting Short-Term KOSPI using Wavelet Transforms and Fuzzy Neural Network  

Shin, Dong-Kun (삼육대학교 컴퓨터학부)
Chung, Kyung-Yong (상지대학교 컴퓨터정보공학부)
Publication Information
The Journal of the Korea Contents Association / v.11, no.6, 2011 , pp. 1-7 More about this Journal
Abstract
The methodology of KOSPI forecast has been considered as one of the most difficult problem to develop accurately since short-term KOSPI is correlated with various factors including politics and economics. In this paper, we presents a methodology for forecasting short-term trends of stock price for five days using the feature selection method based on a neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by removing the worst input features one by one. A technical indicator are selected for preprocessing KOSPI data in the first step. In the second step, thirty-nine numbers of input features are produced by wavelet transforms. Twelve numbers of input features are selected as the minimized numbers of input features from thirty-nine numbers of input features using the non-overlap area distribution measurement method. The proposed method shows that sensitivity, specificity, and accuracy rates are 72.79%, 74.76%, and 73.84%, respectively.
Keywords
Short-Term Trend; Fuzzy Neural Networks; Wavelet Transforms; Feature Selection;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 J. S. Lim, "Finding Fuzzy Rules by Neural Network with Weighted Fuzzy Membership Function," International Journal of Fuzzy Logic and Intelligent Systems, Vol.4, No.2, pp.211-216, 2004.   과학기술학회마을   DOI   ScienceOn
2 K. J. Kim, "Financial Time Series Forecasting using Support Vector Machines," Neurocomputing, Vol.55, Issues 1-2, pp.307-309, 2003.   DOI   ScienceOn
3 J. S. Lim, S. Gupta, "Feature Selection Using Weighted Neuro-Fuzzy Membership Functions," The International Conference on Artificial Intelligence, Vol.1, pp.1301-1315, Las Vegas, Nevada, 2004.
4 S. A. George, P. V. Kimon, "Forecasting Stock Market Short-Term Trends using a Neuro-Fuzzy based Methodology," Expert Systems with Applications, Vol.36, Issue 7, pp.10696-10707, 2009   DOI   ScienceOn
5 M. Suryanarayana, B. Darcy, "Analysis of Feed forward Backpropagation Neural Networks used in Vehicle Detection," Transportation Research Part C: Emerging Tech., Vol.3, Issue 3, pp.161-174, 1995.   DOI   ScienceOn
6 이상홍, 임준식, "KOSPI 예측을 위한 NEWFM 기반의 특징입력 및 퍼지규칙 추출", 한국 인터넷정보학회 논문지, 제9권, 제1호, 2007.   과학기술학회마을
7 K. K. Ang and C. Quek, "Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach," IEEE Trans. Neural Networks, Vol.17, No.5, pp.790-802, 2006.   DOI   ScienceOn
8 J. S. Lim, "Finding Features for Real-Time Premature Ventricular Contraction Detection Using a Fuzzy Neural Network System," IEEE Trans. Neural Networks, Vol.20, Issue 3, pp.522-527, 2009.   DOI   ScienceOn
9 S. H. Chai and J. S. Lim, "Economic Turning Point Forecasting Using Fuzzy Neural Network and Non-Overlap Area Distribution Measurement Method," The Korean Economic Association, Vol.23, No.1, pp.111-130, 2007.
10 T. V. Gestel, "Financial Time Series Prediction Using Least Squares Support Vector Machines Within the Evidence Framework," IEEE Trans. Neural Networks, Vol.12, No.4, pp.809-821, 2001.   DOI   ScienceOn
11 S-M Zhou and J. Q. Gan, "Constructing L2-SVM- Based Fuzzy Classifiers in High-Dimensional Space With Automatic Model Selection and Fuzzy Rule Ranking," IEEE Trans. on Fuzzy Systems, Vol.15, No.3, pp.398-409, 2007.   DOI   ScienceOn
12 T. Ray, H. Yenshan, and C. Charles, "Forecasting S&P 500 Stock Index Futures with a Hybrid AI System," Decision Support Systems, Vol.23, Issue 2, pp.161-174, 1998.   DOI   ScienceOn
13 S. H. Lee, J. S. Lim, "KOSPI Time Series Analysis Using Neural Network with Weighted Fuzzy Membership Functions," LNAI 4953, pp.53-62, 2008.
14 K. J. Kim, "Artificial Neural Networks with Evolutionary Instance Selection for Financial Forecasting," Expert Systems with Applications, Vol.30, Issue 3, pp.519-526, 2006.   DOI   ScienceOn
15 이은진, 민철홍, 김태선, "신경 회로망과 통계적 기법을 이용한 종합주가지수 예측 모형의 개발", 전자공학회 논문지, 제45권, 제5호, 2008.   과학기술학회마을
16 Q. Min "Predicting US Recession with Leading Indicators via Neural Network Models," International Journal of Forecasting, Vol.17, Issue 3, pp.383-401, 2001.   DOI   ScienceOn
17 S. Mallat, "Zero Crossings of a Wavelet Transform," IEEE Trans. Inf. Theory, Vol. 37, No.4, pp.1019-1033, 1991.   DOI   ScienceOn
18 B. Scholkopf, A. J. Smola, R. C. Williamson, P. L. Bartlett, "New Support Vector Algorithms," Neural Computation, Vol.12, pp.1207-1245, 2000.   DOI   ScienceOn