Economic Turning Point Forecasting Using The Fuzzy Neural Network and Non-Overlap Area Distribution Measurement Method

Soo Han Chai and Joon Shik Lim
Additional contact information
Soo Han Chai: Kyungwon University
Joon Shik Lim: Kyungwon University

Korean Economic Review, 2007, vol. 23, 111-130

Abstract: This paper proposes a new forecasting model based on the neural network with weighted fuzzy membership functions (NEWFM) concerning forecasting of turning points in the business cycle by the composite index. NEWFM is a new model of neural networks to improve forecasting accuracy by using self adaptive weighted fuzzy membership functions. The locations and weights of the membership functions are adaptively trained, and then the fuzzy membership functions are combined by the bounded sum. To simplify the forecasting processes, the non-overlap area distribution measurement method is applied to select important features by deleting less important inputs. The implementation of the NEWFM demonstrates an excellent capability in the field of business cycle analysis.

Keywords: neural network; rule extraction; business forecasting; business cycle; turning point (search for similar items in EconPapers)
JEL-codes: C8 E3 (search for similar items in EconPapers)
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://keapaper.kea.ne.kr/RePEc/kea/keappr/KER-200706-23-1-06.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kea:keappr:ker-200706-23-1-06

Access Statistics for this article

Korean Economic Review is currently edited by Kyung Hwan Baik

More articles in Korean Economic Review from Korean Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by KEA ().