A hybrid fuzzy time series forecasting model with 4253HT smoother

Abstract

Forecasting time series data is crucial for predicting upcoming observations, especially in the market and business. Proper actions can be taken when there are some figures on future data, which are predicted based on the previous data. The fusion of fuzzy time series in forecasting has made forecasting using linguistic variables possible. However, the existence of extreme values in the time series data has led to inaccurate forecasting since the values are too large or too small. Hence, this paper proposes a hybrid fuzzy time series forecasting model with the 4253HT smoother to reduce the uncertainty of data. In this study, students’ enrolment data at the University of Alabama are implemented to illustrate the proposed hybrid forecasting model. The results show that the proposed model improves the forecasting performance since the mean square, root mean square, and mean absolute errors have been reduced. In the future, the implementation of data smoothing using the 4253HT smoother can be used in other fuzzy time series and intuitionistic fuzzy time series forecasting models.