Online big data-driven oil consumption forecasting with Google trends

Lean Yu (), Yaqing Zhao, Ling Tang and Zebin Yang

International Journal of Forecasting, 2019, vol. 35, issue 1, 213-223

Abstract: The rapid development of big data technologies and the Internet provides a rich mine of online big data (e.g., trend spotting) that can be helpful in predicting oil consumption — an essential but uncertain factor in the oil supply chain. An online big data-driven oil consumption forecasting model is proposed that uses Google trends, which finely reflect various related factors based on a myriad of search results. This model involves two main steps, relationship investigation and prediction improvement. First, cointegration tests and a Granger causality analysis are conducted in order to statistically test the predictive power of Google trends, in terms of having a significant relationship with oil consumption. Second, the effective Google trends are introduced into popular forecasting methods for predicting both oil consumption trends and values. The experimental study of global oil consumption prediction confirms that the proposed online big-data-driven forecasting work with Google trends improves on the traditional techniques without Google trends significantly, for both directional and level predictions.

Keywords: Google trends; Oil consumption forecasting; Online big data; Supply chain; Artificial intelligence (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (62)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S016920701730136X
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:intfor:v:35:y:2019:i:1:p:213-223

DOI: 10.1016/j.ijforecast.2017.11.005

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().