Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships
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 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.
automatic clustering techniques fuzzy logical relationships fuzzy time series fuzzy sets fuzzy forecasting