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http://dx.doi.org/10.5391/JKIIS.2007.17.4.563

Nonlinear Time Series Prediction Modeling by Weighted Average Defuzzification Based on NEWFM  

Chai, Soo-Han (Department of E-Commerce Software, Kyungwon University)
Lim, Joon-Shik (Department of E-Commerce Software, Kyungwon University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.17, no.4, 2007 , pp. 563-568 More about this Journal
Abstract
This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.
Keywords
NEWFM;
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Times Cited By KSCI : 1  (Citation Analysis)
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