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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Suganthi, L., Samuel, A.A.: Energy models for demand forecasting—a review. Renew. Sustain. Energy Rev. 16(2), 1223–1240 (2012)
Chiroma, H., et al.: A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm. Appl. Soft Comput. 48, 50–58 (2016)
Khan, A., et al.: Forecasting electricity consumption based on machine learning to improve performance: a case study for the organization of petroleum exporting countries (OPEC). Comput. Electr. Eng. 86 (2020). https://doi.org/10.1016/j.compeleceng.2020.106737
OPEC. https://www.opec.org. Accessed 11 Feb 2022
Tylkowski, J., Hoja, M.: Time decomposition and short-term forecasting of hydrometeorological conditions in the south Baltic coastal zone of Poland. MDPI Geosci. J. 68(2), 1–18 (2019). https://doi.org/10.3390/geosciences9020068
ArunKumar, K.E., Kalaga, D.V., Kumar, C.M.S., Chilkoor, G., Kawaji, M., Brenza, T.M.: Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: auto-regressive integrated moving average (ARIMA) and seasonal auto-regressive integrated moving average (SARIMA). Appl. Soft Comput. J. 103, 107–161 (2021). https://doi.org/10.1016/j.asoc.2021.107161
ArunKumar, K.E., Kalaga, D.V., Kumar, C.M.S., Kawaji, M., Brenza, T.M.: Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends. Alexandria Eng. J. 61, 7585–7603 (2022). https://doi.org/10.1016/j.aej.2022.01.011
Jai, G., Mallick, B.: A study of time series models ARIMA and ETS. J. Mod. Educ. Comput. Sci 4, 57–63 (2017)
Chen, P., Niu, A., Liu, D., Jiang, W., Ma, B.: Time series forecasting of temperatures using SARIMA: an example from Nanjing. In: IOP Conference Series: Materials Science and Engineering, Chen, Peng (2018). https://doi.org/10.1088/1757-899x/394/5/052024
He, K., Ji, L., Wu, C.W.D., Tso, K.F.G.: Using SARIMA–CNN–LSTM approach to forecast daily tourism demand, vol. 49, pp. 25–33 (2021). https://doi.org/10.1016/j.jhtm.2021.08.022
Zohair, M., et al.: ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound. Neural Comput. Appl. 33(7), 2929–2948 (2021)
Abdelghafar, S., Goda, E., Darwish, A., Hassanien, A.E.: Satellite lithium-ion battery remaining useful life estimation by coyote optimization algorithm. In: 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 124–129 (2019). https://doi.org/10.1109/ICICIS46948.2019.9014752
OPEC Annual Statistical Bulletin (ASB) (2021). https://asb.opec.org/. Accessed 10 Jan 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-27762-7_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27761-0
Online ISBN: 978-3-031-27762-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)