Next Article in Journal
Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation
Next Article in Special Issue
Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting
Previous Article in Journal
Dynamic Simulation of a Trigeneration Scheme for Domestic Purposes Based on Hybrid Techniques
Previous Article in Special Issue
Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Energies 2016, 9(12), 1014; doi:10.3390/en9121014

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

1
School of Economic Information Engineering, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China
2
Institute of Chinese Payment System, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China
3
School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China
4
College of Electronics and Information Engineering, Sichuan University, 24 South Section 1, Yihuan Road, Chengdu 610065, China
5
Huaan Video Technology Co., Ltd., Building 6, 399 Western Fucheng Avenue, Chengdu 610041, China
6
Department of Viral Vaccine, Chengdu Institute of Biological Products Co., Ltd., China National Biotech Group, 379 Section 3, Jinhua Road, Chengdu 610023, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Wei-Chiang Hong
Received: 30 October 2016 / Revised: 23 November 2016 / Accepted: 25 November 2016 / Published: 1 December 2016
View Full-Text   |   Download PDF [1335 KB, uploaded 1 December 2016]   |  

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. View Full-Text
Keywords: ensemble empirical mode decomposition (EEMD); particle swarm optimization (PSO); relevance vector machine (RVM); kernel methods; crude oil price; energy forecasting ensemble empirical mode decomposition (EEMD); particle swarm optimization (PSO); relevance vector machine (RVM); kernel methods; crude oil price; energy forecasting
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Li, T.; Zhou, M.; Guo, C.; Luo, M.; Wu, J.; Pan, F.; Tao, Q.; He, T. Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels. Energies 2016, 9, 1014.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top