A TRIANGULAR FUZZY LOGIC FRAMEWORK FOR PREDICTING WORLD CARBON DIOXIDE EMISSION

Abstract

Fuzzy logic and fuzzy set theory have been extensively used in different fields since the pioneer work of Zadeh. Several studies can be found in literature regarding the prediction of time series data based on fuzzy logic and fuzzy set theory. The aim of the current study is to forecast the global Carbon dioxide (CO2) emission using triangular fuzzy number. To get the crisp forecast, the centroid method has been used for defuzzification of the fuzzy sets. The results of the proposed method are compared with the state-of-the-art methods in the field of fuzzy time series forecasting, based on the performance metrics i.e., mean square error (MSE) and average forecast error (AFER). The proposed method has produced a minimum mean squared error (MSE) of 1.768, and average forecast error (AFER) of 0.009 as compared to the other competitors. Moreover, to show the performance of the proposed method graphically, the boxplots of absolute forecast error and squared forecast error have also been constructed.