Multi-attribute fuzzy time series method based on fuzzy clustering

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Please use this identifier to cite or link to this item: http://nhuir.nhu.edu.tw/handle/987654321/23541

Title:	Multi-attribute fuzzy time series method based on fuzzy clustering
Authors:	王佳文;Wang, Jia-Wen;Cheng, Ching-Hsue;Cheng, Guang-Wei
Contributors:	電子商務管理學系
Keywords:	Fuzzy time series;Fuzzy clustering;Stock index futures forecasting
Date:	2008-02
Issue Date:	2015-10-05 17:09:07 (UTC+8)
Abstract:	Traditional time series methods can predict the seasonal problem, but fail to forecast the problems with linguistic value. An alternative forecasting method such as fuzzy time series is utilized to deal with these kinds of problems. Two shortcomings of the existing fuzzy time series forecasting methods are that they lack persuasiveness in determining universe of discourse and the length of intervals, and that they lack objective method for multiple-attribute fuzzy time series. This paper introduces a novel multiple-attribute fuzzy time series method based on fuzzy clustering. The methods of fuzzy clustering are integrated in the processes of fuzzy time series to partition datasets objectively and enable processing of multiple attributes. For verification, this paper uses two datasets: (1) the yearly data on enrollments at the University of Alabama, and (2) the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) futures. The forecasting results show that the proposed method can forecast not only one-attribute but also multiple-attribute data effectively and outperform the listing methods.
Relation:	Expert Systems with Applications vol. 34, no. 2 pp.1235-1242
Appears in Collections:	[ Department of Electronic Commerce Management] Periodical Articles

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