A Novel Enrolments Forecasting Model Based On Automatic Clustering Techniques and Time-Variant Fuzzy Logical Relationship Groups

Nghiem Van Tinh, Nguyen Cong Dieu

Most fuzzy forecasting approaches are based on model fuzzy logical relationships according to the past data. In this paper, a hybrid forecasting model based on two computational methods, time-variant fuzzy logical relationship groups and clustering technique, is presented for academic enrolments. Firstly, we use the automatic clustering algorithm to divide the historical data into clusters and adjust them into intervals with unequal lengths. Then, based on the new intervals, we fuzzify all the historical data of the enrolments of the University of Alabama and calculate the forecasted output by the proposed method. Compared to the other methods existing in literature, particularly to the first-order fuzzy time series, our method gets a higher average forecasting accuracy rate than the existing methods.