Fuzzy time series forecasting model based on intuitionistic fuzzy sets and arithmetic rules

• Main Authors: Alam, N.M.F.H.N.B., Ramli, Nazirah, Mohamad, Daud
• Format: Conference Proceeding
• Language: English
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0056946

Forecasting time series data using fuzzy time series has been explored by many researchers since 1993. Intuitionistic fuzzy sets are also considered in recent forecasting models, however the calculation in obtaining the forecasted results is complicated as the max-min composition is used in the forecasting procedure. Therefore, in this paper a forecasting model based on intuitionistic fuzzy sets using simple arithmetic rule is proposed. The proposed model used the frequency density-based method to partition the universe of discourse and used simplified arithmetic rule for calculating the forecasted outputs. Two numerical examples are adopted to illustrate the forecasting model; the student enrollments at the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) data. The performance of this model is evaluated using root mean square error. As compared to other previous models, the error calculated for our proposed model has the lowest error values, thus showing that this model outperforms some of the previous models.