Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships
Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships
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DOI：
10.1016/j.eswa.2009.02.085
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发表时间：
2009-10
期刊：
影响因子：
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通讯作者：
Shyi-Ming Chen;Nai-Yi Wang;Jeng-Shyang Pan
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文献类型：
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作者：
Shyi-Ming Chen;Nai-Yi Wang;Jeng-Shyang Pan
近年来，利用模糊时间序列处理预测问题成为一些研究者关注的研究课题。本文提出了一种基于自动聚类技术和模糊逻辑关系的招生预测新方法。首先，我们提出了一种自动聚类算法，用于将历史登记聚类到不同长度的间隔中。然后，每个获得的区间将被分成p个子区间，其中p大于或等于1。基于新得到的区间和模糊逻辑关系，提出了一种预测阿拉巴马大学招生人数的新方法。与现有方法相比，该方法具有较高的平均预测准确率。
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.