Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

Taiyong Li, Yingrui Zhou, Xinsheng Li, Jiang Wu and Ting He
Additional contact information
Taiyong Li: 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
Xinsheng Li: College of Computer Science, Sichuan University, Chengdu 610064, China
Jiang Wu: School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
Ting He: Architectural Design Institute, Nuclear Power Institute of China, Chengdu 610213, China

Energies, 2019, vol. 12, issue 19, 1-25

Abstract: As one of the leading types of energy, crude oil plays a crucial role in the global economy. Understanding the movement of crude oil prices is very attractive for producers, consumers and even researchers. However, due to its complex features of nonlinearity and nonstationarity, it is a very challenging task to accurately forecasting crude oil prices. Inspired by the well-known framework “decomposition and ensemble” in signal processing and/or time series forecasting, we propose a new approach that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), differential evolution (DE) and several types of ridge regression (RR), namely, ICEEMDAN-DE-RR, for more accurate crude oil price forecasting in this paper. The proposed approach consists of three steps. First, we use the ICEEMDAN to decompose the complex daily crude oil price series into several relatively simple components. Second, ridge regression or kernel ridge regression is employed to forecast each decomposed component. To enhance the accuracy of ridge regression, DE is used to jointly optimize the regularization item, the weights and parameters of each single kernel for each component. Finally, the predicted results of all components are aggregated as the final predicted results. The publicly available West Texas Intermediate (WTI) daily crude oil spot prices are used to validate the performance of the proposed approach. The experimental results indicate that the proposed approach can achieve better performance than some state-of-the-art approaches in terms of several evaluation criteria, demonstrating that the proposed ICEEMDAN-DE-RR is very promising for daily crude oil price forecasting.

Keywords: crude oil prices; time series forecasting; improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN); kernel learning; kernel ridge regression; differential evolution (DE) (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/19/3603/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/19/3603/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:19:p:3603-:d:269322

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().