Predicting Oil price Movements: A Dynamic Artificial Neural Network Approach

Godarzi, A.A.; Madadi Amiri, R.; Talaei, A.; Jamasb, T.

doi:10.1016/j.enpol.2013.12.049

Predicting Oil price Movements: A Dynamic Artificial Neural Network Approach

Godarzi, A.A.; Madadi Amiri, R.; Talaei, A.; Jamasb, T.

Authors

A.A. Godarzi

R. Madadi Amiri

A. Talaei

T. Jamasb

Abstract

Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to develop a NARX model. The NARX model is trained with historical data from 1974 to 2004 and the results are verified with data from 2005 to 2009. The results show that NARX model is more accurate than time series and static ANN models in predicting oil prices in general as well as in predicting the occurrence of oil price shocks.

Citation

Godarzi, A., Madadi Amiri, R., Talaei, A., & Jamasb, T. (2014). Predicting Oil price Movements: A Dynamic Artificial Neural Network Approach. Energy Policy, 68, 371-382. https://doi.org/10.1016/j.enpol.2013.12.049

Journal Article Type	Article
Publication Date	Apr 1, 2014
Deposit Date	Jun 9, 2014
Publicly Available Date	Jun 16, 2014
Journal	Energy Policy
Print ISSN	0301-4215
Publisher	Elsevier
Peer Reviewed	Peer Reviewed
Volume	68
Pages	371-382
DOI	https://doi.org/10.1016/j.enpol.2013.12.049
Keywords	Oil price forecasting, Time series model, NARX model.
Public URL	https://durham-repository.worktribe.com/output/1425732

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Copyright Statement
NOTICE: this is the author’s version of a work that was accepted for publication in Energy Policy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy Policy, 68, 2014, 10.1016/j.enpol.2013.12.049.