Abstract
Petroleum is an essential part of all aspects of today’s technology, and its consumption is strongly correlated with economic growth. This paper presents a forecasting model of petroleum GDP (Gross Domestic Product) for the members of OPEC (Organization of the Petroleum Exporting Countries). The proposed model is presented with the statistical ARIMA (Autoregressive Integrated Moving Average) model which is considered one of the most effective methods for fore-casting stationary time-series data. The proposed model is mainly based on the penalized likelihood method used to obtain the optimal ARIMA parameters. The obtained results show that the proposed model is capable of achieving high accuracy for prediction and short-term forecasting for this type of time series data. Therefore, the proposed model has the potential to be used as an important tool in forecasting petroleum OPEC Members’ GDP, which will facilitate proper monitoring and control of OPEC petroleum consumption and economic growth.
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Abdelghafar, S., Darwish, A., Ali, A. (2023). Short-Term Forecasting of GDP Growth for the Petroleum Exporting Countries Based on ARIMA Model. In: Hassanien, A.E., et al. The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-031-27762-7_37
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DOI: https://doi.org/10.1007/978-3-031-27762-7_37
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