Abstract
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%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55, 307–309 (2003)
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)
Ishibuchi, H., Nakashima, T.: Voting in Fuzzy Rule-Based Systems for Pattern Classification Problems. Fuzzy Sets and Systems 103, 223–238 (1999)
Nauk, D., Kruse, R.: A Neuro-Fuzzy Method to Learn Fuzzy Classification Rules from Data. Fuzzy Sets and Systems 89, 277–288 (1997)
Setnes, M., Roubos, H.: GA-Fuzzy Modeling and Classification: Complexity and Performance. IEEE Trans. Fuzzy Systems 8(5), 509–522 (2000)
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)
Mallat, S.: Zero Crossings of a Wavelet Transform. IEEE Trans. on Information Theory 37, 1019–1033 (1991)
Lim, J.S., Wang, D., Kim, Y.-S., Gupta, S.: A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome. Neurocomputing 69(7-9), 969–974 (2006)
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)
Gestel, T.V., et al.: Financial Time Series Prediction Using Least Squares Support Vector Machines Within the Evidence Framework. IEEE Trans. Neural Networks 12(4), 809–821 (2001)
Kim, K.-j.: Artificial neural networks with evolutionary instance selection for financial forecasting. Expert System with Applications 30, 519–526 (2006)
Tagaki, T., Sugeno, M.: Fuzzy Identification of System and Its Applications to Modeling and Control. IEEE Trans. SMC(15), 116–132 (1985)
Carpenter, G.A., Grossberg, S., Reynolds, J.: ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Networks 4, 565–588 (1991)
Jang, R.: ANFIS: Adaptive network-based fuzzy inference system. IEEE Trans. Syst., Man, Cybern. 23, 665–685 (1993)
Wang, J.S., Lee, C.S.G.: Self-Adaptive Neuro-Fuzzy Inference System for Classification Applications. IEEE Trans., Fuzzy Systems 10(6), 790–802 (2002)
Simpson, P.: Fuzzy min-max neural networks – Part 1: Classification. IEEE Trans. Neural Networks 3, 776–786 (1992)
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)
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)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, SH., Lim, J.S. (2008). KOSPI Time Series Analysis Using Neural Network with Weighted Fuzzy Membership Functions. In: Nguyen, N.T., Jo, G.S., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2008. Lecture Notes in Computer Science(), vol 4953. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78582-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-78582-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78581-1
Online ISBN: 978-3-540-78582-8
eBook Packages: Computer ScienceComputer Science (R0)