Forecasting Enrollments based on Fuzzy Time Series with Higher Forecast Accuracy Rate

The Time-Series models have been used to make predictions in whether forecasting, academic enrollments, etc. Song & Chissom introduced the concept of fuzzy time series in 1993[11]. Over the past 19 years, many fuzzy time series methods have been proposed for forecasting enrollments. But the forecasting accuracy rate of the existing methods is not good enough. These methods have either used enrollment numbers or difference or percentage change of enrollments as the universe of discourse. In this paper, we proposed a method based on fuzzy time series, which gives the higher forecasting accuracy rate than the existing methods. The proposed method used the percentage change as the universe of discourse and mean based partitioning. To illustrate the forecasting process, the historical enrollment of University of Alabama is used. Keywords- fuzzy set, fuzzy time series, time variant model, first order model, forecast error. 1