Next Article in Journal
Internal Model Current Control of Brushless Doubly Fed Induction Machines
Next Article in Special Issue
Power Plant Economic Analysis: Maximizing Lifecycle Profitability by Simulating Preliminary Design Solutions of Steam-Cycle Conditions
Previous Article in Journal
Economic Effects of Wind Power Plant Deployment on the Croatian Economy
Previous Article in Special Issue
A Systems Analysis of the Development Status and Trends of Rural Household Energy in China
Article

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

1
School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
2
Sichuan Province Key Laboratory of Financial Intelligence and Financial Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
3
School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
4
Tianfu College, Southwestern University of Finance and Economics, Mianyang 621000, China
*
Author to whom correspondence should be addressed.
Energies 2018, 11(7), 1882; https://doi.org/10.3390/en11071882
Received: 24 May 2018 / Revised: 9 July 2018 / Accepted: 18 July 2018 / Published: 19 July 2018
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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. View Full-Text
Keywords: crude oil prices; time series forecasting; ensemble empirical mode decomposition (EEMD); sparse Bayesian learning (SBL); energy forecasting crude oil prices; time series forecasting; ensemble empirical mode decomposition (EEMD); sparse Bayesian learning (SBL); energy forecasting
Show Figures

Figure 1

MDPI and ACS Style

Li, T.; Hu, Z.; Jia, Y.; Wu, J.; Zhou, Y. Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning. Energies 2018, 11, 1882. https://doi.org/10.3390/en11071882

AMA Style

Li T, Hu Z, Jia Y, Wu J, Zhou Y. Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning. Energies. 2018; 11(7):1882. https://doi.org/10.3390/en11071882

Chicago/Turabian Style

Li, Taiyong, Zhenda Hu, Yanchi Jia, Jiang Wu, and Yingrui Zhou. 2018. "Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning" Energies 11, no. 7: 1882. https://doi.org/10.3390/en11071882

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop