Fuzzy forecasting based on fuzzy time series

Abstract

This article presents an improved method of fuzzy time series to forecast university enrollments. The historical enrollment data of the University of Alabama were first adopted by Song and Chissom (Song, Q. and Chissom, B. S. (1993). Forecasting enrollment with fuzzy time series-part I, Fuzzy Sets and Systems, 54, 1–9; Song, Q. and Chissom, B. S. (1994). Forecasting enrollment with fuzzy time series-part II, Fuzzy Sets and Systems, 54, 267–277) to illustrate the forecasting process of the fuzzy time series. Later, Chen proposed a simpler method. In this article, we show that our method is as simple as Chen's method but more accurate. In forecasting the enrollment of the University of Alabama, the root mean square percentage error (RMSPE) of our method is 3.1113% while the RMSPE of Chen's method is 4.0516%, which shows that our method is doing much better.

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Keywords:

• Enrollment forecasting
• Fuzzy sets
• Fuzzy time series
• Fuzzy forecasting
• Linguistic variable

Acknowledgement

This research work was partially supported by the National Science Council of the Republic of China under grant No. NSC91-2416-H-019-002.

Notes

E-mail: [email protected]