A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices

Peng Xu · Muhammad Aamir · Ani Shabri · Muhammad Ishaq · Adnan Aslam

30 citations0/0 files storedMathematical Problems in Engineering

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Abstract

Accurate forecasting for the crude oil price is important for government agencies, investors, and researchers. To cope with this issue, in this paper, a new paradigm is designed for the reconstruction of intrinsic mode functions (IMFs) of decomposition and ensemble models to reduce the complexity in computation and to enhance the forecasting accuracy. Decomposition and ensemble methodologies significantly enhance the forecasting accuracy under the framework of “divide and conquer” with the proposed reconstruction of IMFs method. The proposed approach used the autocorrelation at lag 1 of all IMFs for the reconstruction. The ensemble empirical mode decomposition (EEMD) technique is employed to decompose the data into different IMFs. Models that utilized the decomposed data relatively perform well, as compared to its application to the undecomposed data. However, sometimes, the decomposition may produce poor results due to the error accumulation at the end. Thus, in this study, the reconstruction of IMFs is proposed for minimizing the aforementioned error, thereby increasing the forecasting accuracy. The Brent and West Texas Intermediate (WTI) datasets (daily and weekly) are exploited to compare the forecasting performance of autoregressive integrated moving average (ARIMA) along with artificial neural network (ANN) models with the decomposed data. The results have proven that the new paradigm of reconstruction of IMFs through autocorrelation was a better and simple strategy that significantly improved the performance of single models including ARIMA and ANN. Hence, it is concluded that the proposed model takes less computational time and achieved higher forecasting accuracy with the reconstruction of IMFs as opposed to using all IMFs.

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References

1971Time Series Analysis Forecasting and Control.1996Efficient Tests for an Autoregressive Unit Root 2003Time series forecasting using a hybrid ARIMA and neural network model 1998Forecasting with artificial neural networks:2008Introduction to time series analysis and forecasting 2003Neural network forecasting for seasonal and trend time series 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm 2008Improving artificial neural networks’ performance in seasonal time series forecasting

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