INTER-QUARTILE RANGE APPROACH TO LENGTH–INTERVAL ADJUSTMENT OF ENROLLMENT DATA IN FUZZY TIME SERIES FORECASTING

Abstract

Various methods have been presented to investigate the length of data interval and partition number of universe of discourse in fuzzy time series to achieve high level forecasting accuracy. However, the interval length is still an issue and thus, influencing the forecasting accuracy. This paper proposes a new approach using the average inter-quartile range to improve the interval length and subsequently the partition number of universe of discourse. Moreover, in forecasting method, the first-differencing data is also considered to obtain better forecast. The enrollment data of Alabama University is used as an example and the efficiency of the proposed method is compared with the previous methods. The result shows that the proposed method improves the accuracy and efficiency of the yearly enrollment forecasting opportunities.