Fuzzy time series has received increasing intentions since the first definition of fuzzy time series was introduced in 1993 by Song and Chissom. Then many studies proposed new fuzzy time series models. One of the steps in developing fuzzy time series model is partitioning the universe of discourse into different lengths of intervals. However, the performance of fuzzy time series model can be affected by interval length factor. Therefore, the objective of this study is to investigate the performance of Weighted Subsethood fuzzy time series model with different lengths of interval. By using the familiar data in fuzzy time series study, the historical enrollments of University of Alabama data, seven different number of interval cases were generated. The results show that partitioning the data into four intervals has the minimum forecasting error.

Topics
Time series analysis
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