Forecasting World Crude Oil Prices using the Fuzzy Time Series Method with a Comparison of the Chen and Lee Model (Journal of Advances in Information Systems and Technology, 2020)
In this study, the Fuzzy Time Series (FTS) method compared to Chen and Lee models is used to predict world crude oil prices. The goal is to determine which model results are best between Chen and Lee models in the fuzzy time series method in predicting world crude oil prices. In the calculation of FTS number and width specified intervals beginning of the process, the process is very influential to the outcome prediction. The method for determining the number and width of the interval that effectively is by using Rules Sturgess. So that the formation of fuzzy logical relationships will be appropriate and effective yield predictive results. Of the 50 trials that have been done using daily data from the Organization of the Petroleum Exporting Countries (OPEC), it is known that the FTSLee model can predict better than the Chen model with a comparison of the results of the AFER fuzzy time series Lee model by 97.4% and RMSE of 1.617 and the results of the AFER fuzzy time series Chen model by 97.2% and RMSE of 1.693.