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

Document Type: Original Manuscript

Authors

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

10.22094/joie.2018.538229

Abstract

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

Highlights

  • 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.

Keywords


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