Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships

被引：97

作者：

Chen, Shyi-Ming ^{[1
,2
]}

Wang, Nai-Yi ^{[1
]}

Pan, Jeng-Shyang ^{[3
]}

机构：

[1] Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan

[2] Jinwen Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei Cty, Taiwan

[3] Natl Kaohsiung Univ Appl Sci, Dept Elect Engn, Kaohsiung 807, Taiwan

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 08期

关键词：

Automatic clustering techniques; Fuzzy sets; Fuzzy time series; Fuzzy forecasting; Fuzzy logical relationships; TIME-SERIES; TEMPERATURE PREDICTION; GENETIC ALGORITHMS; INTERVALS; LENGTHS; MODELS;

D O I：

10.1016/j.eswa.2009.02.085

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

In recent years, some researchers focused on the research topic of using fuzzy time series to handle forecasting problems. In this paper, we present a new method to forecast enrollments based on automatic clustering techniques and fuzzy logical relationships. First, we present an automatic clustering algorithm for clustering historical enrollments into intervals of different lengths. Then, each obtained interval will be divided into p sub-intervals, where p >= 1. Based on the new obtained intervals and fuzzy logical relationships, we present a new method for forecasting the enrollments of the University of Alabama. The proposed method gets a higher average forecasting accuracy rate than the existing methods. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：11070 / 11076

页数：7

共 25 条

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[2]

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