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A Hybrid GA-FEEMD for Forecasting Crude Oil Prices

Affiliations

  • Faculty of Sciences, Department of Mathematical Sciences, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

Abstract


Forecasting crude oil prices is essential but usually involves a difficult process. In this paper, we proposed a hybrid Genetic Algorithm and Fast Ensemble Empirical Mode Decomposition (GA-FEEMD) for forecasting crude oil price time series data. The proposed GA-FEEMD basically involves three steps. Firstly, we decomposed the original crude oil price time series data into two Intrinsic Mode Functions (IMFs) using FEEMD algorithm. Then, we forecasted the second IMF which basically is the intrinsic trend of the crude oil prices. Then, we applied Genetic Algorithm (GA) to obtain the stopping criterion in the FEEMD process. The hybrid GA-FEEMD forecasting model was compared with ARIMA and Artificial Neural Network methods. The results showed that the proposed GA-FEEMD model improved the forecasting accuracy of the crude oil price time series data.

Keywords

Crude Oil Price Forecast, Fast Ensemble Empirical Mode Decomposition (FEEMD), Genetic Algorithm (GA) and Hybridization

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