Farrokhi, A., Hassanzadeh, R. (2017). Time Series Models to Predict the Monthly and Annual Consumption of Natural Gas in Iran. Journal of Industrial Strategic Management, 2(2), 67-76.
Arash Farrokhi; Reza Hassanzadeh. "Time Series Models to Predict the Monthly and Annual Consumption of Natural Gas in Iran". Journal of Industrial Strategic Management, 2, 2, 2017, 67-76.
Farrokhi, A., Hassanzadeh, R. (2017). 'Time Series Models to Predict the Monthly and Annual Consumption of Natural Gas in Iran', Journal of Industrial Strategic Management, 2(2), pp. 67-76.
Farrokhi, A., Hassanzadeh, R. Time Series Models to Predict the Monthly and Annual Consumption of Natural Gas in Iran. Journal of Industrial Strategic Management, 2017; 2(2): 67-76.
Time Series Models to Predict the Monthly and Annual Consumption of Natural Gas in Iran
1Department of Industrial Engineering, University College of Ayandegan, Tonekabon, Iran
2Department of Industrial Engineering, University College of Rouzbahan, Sari,, Iran, Iran
Abstract
Considering the fact that natural gas is a widely used energy source, the prediction of its consumption can be useful (Derek LAM, 2013). As Iran has one of the largest gas reserves in the world, its consumption in the country can affect the worldwide price of gas, Therefore, the current research is useful both from economic and environmental point of view. The goal of the study is to select the best model for the prediction of gas consumption. To achieve the goal time series analysis are used. The findings indicate that ARIMA (0, 1, 0) is the best model for the prediction of annual gas consumption, while SARIMA (1, 0, 0) (1, 1, 0) for the prediction of monthly gas consumption
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