Cardinality-based fuzzy time series for forecasting enrollments

Authors:
Jing-Rong Chang;Ya-Ting Lee;Shu-Ying Liao;Ching-Hsue Cheng
Affiliations:
Department of Information Management, Chaoyang University of Technology, Wufong Township, Taichung County, Taiwan;Department of Information Management, National Yunlin University of Science and Technology, Touliu, Yunlin, Taiwan;Department of Information Management, National Yunlin University of Science and Technology, Touliu, Yunlin, Taiwan;Department of Information Management, National Yunlin University of Science and Technology, Touliu, Yunlin, Taiwan
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

Citing 4
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Abstract

Forecasting activities are frequent and widespread in our life. Since Song and Chissom proposed the fuzzy time series in 1993, many previous studies have proposed variant fuzzy time series models to deal with uncertain and vague data. A drawback of these models is that they do not consider appropriately the weights of fuzzy relations. This paper proposes a new method to build weighted fuzzy rules by computing cardinality of each fuzzy relation to solve above problems. The proposed method is able to build the weighted fuzzy rules based on concept of large itemsets of Apriori. The yearly data on enrollments at the University of Alabama are adopted to verify and evaluate the performance of the proposed method. The forecasting accuracies of the proposed method are better than other methods.