Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Taiyong Li, Zhenda Hu, Yanchi Jia, Jiang Wu and Yingrui Zhou
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Taiyong Li: School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
Zhenda Hu: School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
Yanchi Jia: Tianfu College, Southwestern University of Finance and Economics, Mianyang 621000, China
Jiang Wu: School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
Yingrui Zhou: School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China

Energies, 2018, vol. 11, issue 7, 1-23

Abstract: Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However, due to the extreme nonlinearity and nonstationarity of crude oil prices, it is a challenging task for the traditional methodologies of time series forecasting to handle it. To address this issue, in this paper, we propose a novel approach that incorporates ensemble empirical mode decomposition (EEMD), sparse Bayesian learning (SBL), and addition, namely EEMD-SBL-ADD, for forecasting crude oil prices, following the “decomposition and ensemble” framework that is widely used in time series analysis. Specifically, EEMD is first used to decompose the raw crude oil price data into components, including several intrinsic mode functions (IMFs) and one residue. Then, we apply SBL to build an individual forecasting model for each component. Finally, the individual forecasting results are aggregated as the final forecasting price by simple addition. To validate the performance of the proposed EEMD-SBL-ADD, we use the publicly-available West Texas Intermediate (WTI) and Brent crude oil spot prices as experimental data. The experimental results demonstrate that the EEMD-SBL-ADD outperforms some state-of-the-art forecasting methodologies in terms of several evaluation criteria such as the mean absolute percent error (MAPE), the root mean squared error (RMSE), the directional statistic (Dstat), the Diebold–Mariano (DM) test, the model confidence set (MCS) test and running time, indicating that the proposed EEMD-SBL-ADD is promising for forecasting crude oil prices.

Keywords: crude oil prices; time series forecasting; ensemble empirical mode decomposition (EEMD); sparse Bayesian learning (SBL); energy forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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