Three Approaches to Time Series Forecasting of Petroleum Demand in OECD Countries

Document Type: Original Manuscript


Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran



Petroleum (crude oil) is one of the most important resources of energy and its demand and consumption is growing while it is a non-renewable energy resource. Hence forecasting of its demand is necessary to plan appropriate strategies for managing future requirements. In this paper, three types of time series methods including univariate Seasonal ARIMA, Winters forecasting and Transfer Function-noise (TF) models are used to forecast the petroleum demand in OECD countries. To do this, we use the demand data from January 2001 to September 2010 and hold out data from October 2009 to September 2010 to test the sufficiency of the forecasts. For the TF model, OECD petroleum demand is modeled as a function of their GDP. We compare the root mean square error (RMSE) of the fitted models and check what percentage of the testing data is covered by the confidence intervals (C.I.). Accordingly we conclude that Transfer Function model demonstrates a better forecasting performance.

Graphical Abstract

Three Approaches to Time Series Forecasting of Petroleum Demand in OECD Countries


  • Two Seasonal ARIMA, a Winters and a Transfer Function model are fitted to the data.
  • GDP is used in TF model as an influential variable on petroleum demand.
  • The best model is selected based on RMSE and number of observations covered by CI.
  • Univariate models (ARIMA and Winters) generally underestimate the testing data.
  • The multivariate model (TF) provides more accurate forecasts than univariate ones.


Akkurt, M., Demirel, O.F. and Zaim, S. (2010). Forecasting Turkey’s natural gas consumption by using time series methods. European Journal of Economic and Political Studies, 3, 1-21.

Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (1994). Time series analysis forecasting and control. 3rd edition, Prentice-Hall International, New Jersey.

Cheong, C.W. (2009). Modeling and forecasting crude oil markets using ARCH-type models. Energy Policy, 37, 2346-2355.

Gooijer, J.G.D. and Hyndman, R.J. (2006). 25 years of time series forecasting. International Journal of Forecasting, 22, 443-473.

Huntington, H.G. (2011). Backcasting U.S. oil demand over a turbulent decade. Energy Policy, 39, 5674-5680.

Jiping, X. and Ping, W. (2008). An analysis of forecasting model of crude oil demand based on cointegration and vector error correction model (VEC). International Seminar on Business and Information Management, 485-488.

Montgomery, D.C., Jennings, C.L. and Kulahci, M. (2008). Introduction to time series analysis and forecasting. John Wiley & Sons, New Jersey.

Pankratz, A. (1983). Forecasting with univariate Box-Jenkins models- concepts and cases. John Wiley & Sons, New York.

Pedregal, D.J., Dejuan, O., Gomez, N. and Tobarra, M.A. (2009). Modelling demand for crude oil products in Spain. Energy Policy, 37, 4417-4427.

U.S. Energy Information Administration (EIA), 2011.

Ye, M., Zyren, J. and Shore, J. (2005). A monthly crude oil spot price forecasting model using relative inventories. International Journal of Forecasting, 21, 491-501.