KOSPI Time Series Analysis Using Neural Network with Weighted Fuzzy Membership Functions
Fuzzy neural networks have been successfully applied to generate predictive rules for stock forecasting. This paper presents a methodology to forecast the daily Korea composite stock price index (KOSPI) by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and the minimized number of input features using the distributed non-overlap area measurement method. NEWFM supports the KOSPI time series analysis based on the defuzzyfication of weighted average method which is the fuzzy model suggested by Takagi and Sugeno. NEWFM classifies upward and downward cases of next day’s KOSPI using the recent 32 days of CPPn,m (Current Price Position of day n : a percentage of the difference between the price of day n and the moving average of the past m days from day n-1) of KOSPI. In this paper, the Haar wavelet function is used as a mother wavelet. The most important five input features among CPPn,m and 38 numbers of wavelet transformed coefficients produced by the recent 32 days of CPPn,m are selected by the non-overlap area distribution measurement method. For the data sets, from 1991 to 1998, the proposed method shows that the average of accuracy rate is 67.62%.
Keywordsfuzzy neural networks weighted average defuzzification wavelet transform KOSPI nonlinear time series
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- 1.Wang, X., Phua, P.K.H., Lin, W.: Stock market prediction using neural networks: Does trading volume help in short-term prediction? In: Neural Networks, 2003. Proceedings of the International Joint Conference on Volume 4, July 20-24, 2003, vol. 4, pp. 2438–2442 (2003)Google Scholar
- 3.Lim, J.S., Ryu, T.-W., Kim, H.-J., Gupta, S.: Feature Selection for Specific Antibody Deficiency Syndrome by Neural Network with Weighted Fuzzy Membership Functions. In: Wang, L., Jin, Y. (eds.) FSKD 2005. LNCS (LNAI), vol. 3614, pp. 811–820. Springer, Heidelberg (2005)Google Scholar
- 7.Lim, J.S., Gupta, S.: Feature Selection Using Weighted Neuro-Fuzzy Membership Functions. In: The 2004 International Conference on Artificial Intelligence(IC-AI 2004), Las Vegas, Nevada, USA, June 21-24, 2004, vol. 1, pp. 261–266 (2004)Google Scholar
- 10.Bergerson, K., Wunsch, D.C.: A commodity trading model based on a neural network-Expert system hybrid. In: Proceedings of the IEEE International Conference on Neural Networks, pp. I289–I293 (1991)Google Scholar
- 13.Tagaki, T., Sugeno, M.: Fuzzy Identification of System and Its Applications to Modeling and Control. IEEE Trans. SMC(15), 116–132 (1985)Google Scholar
- 18.Lim, J.S.: Finding Fuzzy Rules by Neural Network with Weighted Fuzzy Membership Function. International Journal of Fuzzy Logic and Intelligent Systems 4(2), 211–216 (2004)Google Scholar
- 19.Chai, S.H., Lim, J.S.: Economic Turning Point Forecasting Using Fuzzy Neural Network and Non-Overlap Area Distribution Measurement Method. The Korean Economic Association 23(1), 111–130 (2007)Google Scholar