Predicting Malaysian crude palm oil prices using intuitionistic fuzzy time series forecasting model / Nik Muhammad Farhan Hakim Nik Badrul Alam, Nazirah Ramli and Asyura Abd Nassir

Crude palm oil (CPO) is one of the commodities in Malaysia that highly contributes to economic growth. Since CPO prices fluctuate over years, it is significant to accurately forecast CPO prices in the future to avoid losses. The objective of this paper is to propose an intuitionistic fuzzy time series forecasting model to forecast Malaysian CPO prices. The proposed model uses a defuzzification formula that fully utilizes the main properties of the intuitionistic fuzzy set (IFS), which include the membership and nonmembership functions. The proposed formula has the advantage of handling uncertainty in the time series data. Based on the results, the proposed model has reduced the forecasting error compared to the existing IFS-based models. The mean square error, root mean square error, and mean absolute error were reduced from 1.57% to 24.93%, 0.79% to 11.77%, and 1.34% to 10.39%, respectively. It is recommended that the decision-makers from respective parties make the right decision based on the forecasted prices to maintain Malaysia as one of the largest CPO producers.