Extracting Input Features and Fuzzy Rules for forecasting KOSPI Stock Index Based on NEWFM


Sang-Hong Lee, Joon-S. Lim, Journal of Information Processing Systems Vol. 9, No. 1, pp. 129-136, Feb. 2008  


Keywords: Fuzzy Neural Networks, Rule Extraction, Wavelent Transforms, NEWFM, KOSPI
Fulltext:

Abstract

This paper presents a methodology to forecast KOSPI index 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 classifies upward and downward cases of KOSPI using the recent 32 days of CPPn,m (Current Price Position of day n for n-1 to n-m days) of KOSPI. The five most important input features among CPPn,m and 38 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 forecast rate is 67.62%.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Lee, S. & Lim, J. (2008). Extracting Input Features and Fuzzy Rules for forecasting KOSPI Stock Index Based on NEWFM. Journal of Internet Computing and Services, 9(1), 129-136.

[IEEE Style]
S. Lee and J. Lim, "Extracting Input Features and Fuzzy Rules for forecasting KOSPI Stock Index Based on NEWFM," Journal of Internet Computing and Services, vol. 9, no. 1, pp. 129-136, 2008.

[ACM Style]
Sang-Hong Lee and Joon-S. Lim. 2008. Extracting Input Features and Fuzzy Rules for forecasting KOSPI Stock Index Based on NEWFM. Journal of Internet Computing and Services, 9, 1, (2008), 129-136.