A new method to forecast enrollments using fuzzy time series and clustering techniques

This paper presents a new method to forecast enrollments using fuzzy time series and clustering techniques. First, we present an automatic clustering algorithm to partition the universe of discourse into different lengths of intervals. Then, we present a new method for forecasting enrollments using fuzzy time series and the proposed clustering algorithm. The historical data of the University of Alabama are used to illustrate the forecasting process of the proposed method. The experimental results show that the proposed method gets a higher average forecasting accuracy rate than the existing methods.

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