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

Journal of Optimization in Industrial Engineering

مقاله 3، دوره 11، شماره 2 - شماره پیاپی 24، مهر 2018، صفحه 17-24   

نوع مقاله: Original Manuscript

شناسه دیجیتال (DOI): 10.22094/joie.2018.538229

نویسندگان

Majid Khedmati^* ؛ Babak Ghalebsaz-Jeddi

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

چکیده

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.

کلیدواژه‌ها

Time series forecasting؛ OECD countries؛ Petroleum demand

مراجع

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. 3^rd 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. http://www.eia.gov/emeu/ipsr/demand.html.

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.