Fast ensemble empirical mode decomposition model for forecasting crude oil and condensates

Abstract

Crude oil and condensates supply and demand strives to be main authority of the sustenance of almost all country’s economy. The sudden rise in the oil price has forced the government to forecast the supply and demand of crude oil and condensates in order to make sure that the amount of crude oil meets the supply and demand of the country. Accurate forecasts can save cost, foresee scarcity of demand, and help in budgeting profit. In addition, predicting crude oil and condensate data is frequently proven to be a demanding task considering the various intricacies of oil data pattern. The main objective of this study was to forecast crude oil and condensates demand data in Malaysia using Fast Ensemble Empirical Mode Decomposition (FEEMD) model. The forecasting process using FEEMD model was performed in order to achieve the most desirable forecast accuracy of the crude oil and condensates data. The FEEMD model is an extension of the Empirical Mode Decomposition (EMD) model whereby white noise signal was added to the existing signal in the sifting process. The effectiveness of the proposed forecasting method was compared to other traditional models of ARIMA, ARIMAX and GARCH. The results revealed that the proposed FEEMD method for forecasting crude oil and condensates data was very promising as it achieved good forecast accuracy.