Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels

Taiyong Li, Min Zhou, Chaoqi Guo, Min Luo, Jiang Wu, Fan Pan, Quanyi Tao and Ting He
Additional contact information
Taiyong Li: School of Economic Information Engineering, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China
Min Zhou: School of Economic Information Engineering, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China
Chaoqi Guo: School of Economic Information Engineering, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China
Min Luo: School of Economic Information Engineering, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China
Jiang Wu: School of Economic Information Engineering, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China
Fan Pan: College of Electronics and Information Engineering, Sichuan University, 24 South Section 1, Yihuan Road, Chengdu 610065, China
Quanyi Tao: Huaan Video Technology Co., Ltd., Building 6, 399 Western Fucheng Avenue, Chengdu 610041, China
Ting He: Department of Viral Vaccine, Chengdu Institute of Biological Products Co., Ltd., China National Biotech Group, 379 Section 3, Jinhua Road, Chengdu 610023, China

Energies, 2016, vol. 9, issue 12, 1-21

Abstract: Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD), adaptive particle swarm optimization (APSO), and relevance vector machine (RVM)—namely, EEMD-APSO-RVM—to predict crude oil price based on the “decomposition and ensemble” framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs) and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO) was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI) to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE), mean absolute percent error (MAPE), and directional statistic (Dstat), showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price.

Keywords: ensemble empirical mode decomposition (EEMD); particle swarm optimization (PSO); relevance vector machine (RVM); kernel methods; crude oil price; 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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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