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Title

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

دوفصلنامه بهینه سازی در مهندسی صنایع، دوره: 11، شماره: 2
Year: 1397
COI: JR_JOIE-11-2_003
Language: EnglishView: 274
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Authors

Majid Khedmati - Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Babak Ghalebsaz-Jeddi - Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran

Abstract:

Petroleum (crude oil) is one of the most important resources of energy and its demand and consumption is growing while it is a nonrenewableenergy 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

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Khedmati, Majid and Ghalebsaz-Jeddi, Babak,1397,Three Approaches to Time Series Forecasting of Petroleum Demand in OECD Countries,https://civilica.com/doc/791064

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Type of center: دانشگاه دولتی
Paper count: 14,811
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