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论文类型：期刊论文

发表时间：2013-10-15

发表刊物：EXPERT SYSTEMS WITH APPLICATIONS

收录刊物：SCIE、EI

卷号：40

期号：14

页面范围：5673-5679

ISSN号：0957-4174

关键字：Forecasting; Fuzzy time series; Information granule; Enrollment

摘要：Partitioning the universe of discourse and determining effective intervals are critical for forecasting in fuzzy time series. Equal length intervals used in most existing literatures are convenient but subjective to partition the universe of discourse. In this paper, we study how to partition the universe of discourse into intervals with unequal length to improve forecasting quality. First, we calculate the prototypes of data using fuzzy clustering, then form some subsets according to the prototypes. An unequal length partitioning method is proposed. We show that these intervals carry well-defined semantics. To verify the suitability and effectiveness of the approach, we apply the proposed method to forecast enrollment of students of Alabama University and Germany's DAX stock index monthly values. Empirical results show that the unequal length partitioning can greatly improve forecast accuracy. Further more, the proposed method is very robust and stable for forecasting in fuzzy time series. (C) 2013 Elsevier Ltd. All rights reserved.