Enrollment forecasting based on modified weight fuzzy time series

Abstract

Many different methods and models have been proposed by researchers using fuzzy time series for many different applications. The main issue in forecasting is in improving forecast accuracy. This paper presents the development of weight fuzzy time series based on a collection of variation of the chronological number in the Fuzzy Logical Group (FLG). The aim here is to develop an appropriate weight on fuzzy time series for forecasting of trend series data. A data set of university enrollment for Alabama University and Universiti Teknologi Malaysia (UTM) are used for forecasting. Results from this study shows that the proposed approach gave a lot of improvement. The forecasting fitness function used are the Means Square Error (MSE) and average error.