A novel hybrid model for forecasting crude oil price based on time series decomposition

Hooman Abdollahi

Applied Energy, 2020, vol. 267, issue C, No S030626192030547X

Abstract: Oil price forecasting has received a prodigious attention by scholars and policymakers due to its significant effect on various economic sectors and markets. Incentivized by this issue, the author proposes a novel hybrid model for crude oil price forecasting whose focus is on improving the accuracy of prediction taking into consideration the characteristics existing in the oil price time series. In so doing, the author constitutes a hybrid model consisting of complete ensemble empirical mode decomposition, support vector machine, particle swarm optimization, and Markov-switching generalized autoregressive conditional heteroskedasticity to capture the nonlinearity and volatility of the time series more effectively. Mean absolute error, root mean square error, and mean absolute percentage error tests are used to measure forecasting errors. Results robustness and forecasting quality of the proposed hybrid model compared with counterparts are also investigated by Diebold-Mariano test. Finally, empirical results demonstrate that the proposed hybrid model outperforms other models.

Keywords: Oil price forecasting; Time series decomposition; Particle swarm optimization; Markov-switching GARCH; Support vector machine (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (31)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626192030547X
Full text for ScienceDirect subscribers only

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:eee:appene:v:267:y:2020:i:c:s030626192030547x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.115035

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().