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IJIT
2004

Forecasting Enrollment Model Based on First-Order Fuzzy Time Series

10 years 4 months ago
Forecasting Enrollment Model Based on First-Order Fuzzy Time Series
-- This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical data are linguistic values. It is shown that proposed time-invariant method improves the performance of forecasting process. Further, the effect of using different number of fuzzy sets is tested as well. As with the most of cited papers, historical enrollment of the University of Alabama is used in this study to illustrate the forecasting process. Subsequently, the performance of the proposed method is compared with existing fuzzy time series time-invariant models based on forecasting accuracy. It reveals a certain performance superiority of the proposed method over methods described in the literature. Keywords -- Forecasting, fuzzy time series, linguistic values, student enrollment, time-invariant model.
Melike Sah, Konstantin Y. Degtiarev
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where IJIT
Authors Melike Sah, Konstantin Y. Degtiarev
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