NDLI: A new method to forecast the TAIEX based on fuzzy time series

Content Provider	IEEE Xplore Digital Library
Author	Chao-Dian Chen Shyi-Ming Chen
Copyright Year	2009
Description	Author affiliation: Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R. O. C. (Chao-Dian Chen; Shyi-Ming Chen)
Abstract	In this paper, we present a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series, where the main factor is the TAIEX and the secondary factors are either the Dow Jones, the NASDAQ, the M (Taiwan), or their combinations. First, we fuzzify the historical data of the main factor into fuzzy sets with a fixed length of intervals to form fuzzy logical relationships. Then, we group the fuzzy logical relationships into fuzzy logical relationship groups. Then, we evaluate the leverage of fuzzy variations between the main factor and the secondary factor to forecast the TAIEX. The experimental results show that the proposed method gets a higher average forecasting accuracy rate than Chen's method [1] and Huarng et al.'s method [9] to forecast the TAIEX.
Starting Page	3450
Ending Page	3455
File Size	190139
Page Count	6
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346230
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Fuzzy set theory Fuzzy sets Technology forecasting Fuzzy systems Computer science Stock markets Cybernetics USA Councils Chaos fuzzy variation fuzzy sets fuzzy time series fuzzy logical relationships
Content Type	Text
Resource Type	Article

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