Presenting a model for predicting global crude oil prices based on artificial intelligence algorithm

Articles in Press

Document Type : Original Article

Authors

Department of Economics, Central Tehran Branch, Islamic Azad university, Tehran, Iran

Abstract

Predicting the price trend of crude oil and its fluctuations has always been one of the challenges facing traders in oil markets. In this regard, in order to make rational predictions about the future, efforts have been made to establish and expand quantitative and qualitative forecasting methods. Therefore, forecasting methods have always been important tools for future researchers. Consequently, this study aims to investigate and evaluate algorithms, especially machine learning algorithms (such as Bayesian networks, artificial neural networks, support vector machines, k-nearest neighbors), for predicting crude oil prices. For this current research, variables such as European Union oil prices and U.S. oil prices from 1990 to 2020 are considered. Python software was used for analysis.The results of this study have shown that, based on the machine learning algorithms used in the first model (Brent crude oil price), algorithms like Linear Regression, Least Angle Regression, Orthogonal Matching Pursuit, and Bayesian Ridge are the best algorithms for predicting the Brent crude oil price. Additionally, the results of machine learning algorithms in the second model (global Brent oil price and West Texas oil price) indicate that Linear Regression, Least Angle Regression, Orthogonal Matching Pursuit, and Bayesian Ridge are the best algorithms for predicting West Texas oil prices.

Keywords

References

[1] Hasan, M. Abedin, M. and hajek, p.  Sultan, N. and Lucey, BM. (2022).A Blending Ensemble Learning Model for Crude Oil Price Prediction. Available at SSRN: https://ssrn.com/abstract=4153206 or http://dx.doi.org/10.2139/ssrn.4153206
[2] Sen, A, Dutta Choudhury, K. Kumar Datta, T. (2023) An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach. PLoS ONE 18(3): e0268996. https://doi.org/10.1371/journal.pone.0268996
[3] He, H. Sun, M. Li, X.  & Mensah, I, (2022). A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features. Energy, Elsevier, vol. 244. https://doi.org/10.1016/j.energy.2021.122706
[4] Karathanasopoulos, A. Zaremba, A.  Osman, M. and Mikutowski, M. (2019). Oil Forecasting Using Artificial Intelligence. Theoretical Economics Letters, 9, 2283-2290. https://doi.org/10.4236/tel.2019.97144
[5] Manickavasagam J, Visalakshmi S, Apergis N. (2020). A novel hybrid approach to forecast crude oil futures using intraday data. Technol Forecast Soc Change. 158:120126. https://doi.org/10.1016/j.techfore.2020.120126
[6] Jovanovic, L. DejanJovanovic, N. Jovancai Stakic, A. Milos, A.  Magd, H.  AND ET AL.  (2022). "Multi-Step Crude Oil Price Prediction Based on LSTM Approach Tuned by Salp Swarm Algorithm with Disputation Operator" Sustainability 14 (21), 14616. https://doi.org/10.3390/su142114616
[7] Jahanshahi, H. Süleyman, U. Sezgin, K. Qijia, Y.  and Madini O. Alassafi. (2022). "Artificial Intelligence-Based Prediction of Crude Oil Prices Using Multiple Features under the Effect of Russia–Ukraine War and COVID-19 Pandemic" Mathematics 10(22), 4361. https://doi.org/10.3390/math10224361
[8] Lu, Q., Sun, S., Duan, H. et al. (2021).Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model. Energy Inform 4 (Suppl 2), 47. https://doi.org/10.1186/s42162-021-00166-4
[9] Zhao, L. Wang, Z. Wang, S &  He, Y. (2021) Predicting Oil Prices: An Analysis of Oil Price Volatility Cycle and Financial Markets, Emerging Markets Finance and Trade, 57:4, 1068-1087, https://doi.org/10.1080/1540496X.2019.1706045
[10] Kpodar, K.  & Imam, P.(2020). To Pass (or Not to Pass) Through International Fuel Price Changes to Domestic Fuel Prices in Developing Countries: What Are the Drivers?, International Monetary Fund, 20, 1-35. https://doi.org/10.1016/j.enpol.2020.111999
[11] Huang, L., Wang, J. (2018). Global crude oil price prediction and synchronization-based accuracy evaluation using random wavelet neural network. Energy, 151, 875–888. https://doi.org/10.1016/j.energy.2018.03.099
[12] Mohammadi Almoti, M., Hadadi, M., & Nadimi, Y. (2020). Investigating the Weak Efficiency Hypothesis in Two Low-Volatility and High-Volatility Regimes of the OPEC Crude Oil Market. Investment Knowledge, 9(33), 109-127.
[13] Bristone, M. Prasad, R. & Abubakr, A. .(2020). CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms, Petroleum, https://doi.org/10.1016/j.petlm.2019.11.009.
[14] Shabri, A. and Samsudin, R. (2014). Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model, Hindawi Publishing Corporation Mathematical Problems in Engineering. https://doi.org/10.1155/2014/201402
[15] Asghari, H., Omid, M., Malekinejad, Z., & Omid, A. (2018). Forecasting global crude oil demand by using vector autoregressive, autoregressive distributed lag, and gravitational search algorithms. Future Studies in Management, 29(4), 101-118.
[16] Elyakov, A. and et al.(2018). World oil prices: a retrospective analysis and elevation of factors, Corpus ID: 54783239.
[17] Peng, J. and et al. (2020). Dynamic Characteristics of Crude Oil Price Fluctuation—From the Perspective of Crude Oil Price Influence Mechanism, Energies, 13, 4465; https://doi.org/10.3390/en13174465
[18] Liu, Z.and et al .(2018). Financial factors affecting oil price change and oil-stock interactions: a review and future perspectives, Natural Hazards, https://doi.org/10.1007/s11069-018-3473-y.
[19] Sehgal, N.   &  Pandey, K.K. (2015). Artificial intelligence methods for oil price forecasting: a review and evaluation, Energy Syst, https://doi.org/10.1007/s12667-015-0151-y
[20] Gao, S. & Lei, Y.(2017). A new approach for crude oil price prediction based on stream Learning, Geoscience Frontiers 8, 183-187. https://doi.org/10.1016/j.gsf.2016.08.002
[21] Takroosta A, Mohajeri P, Mohammadi T, Shakeri A,  & Ghasemi A. (2019). An Analysis of Oil Prices Considering the Political Risk of OPEC. Jemr, 10 (37), 105-138. http://jemr.khu.ac.ir/article-1-1832-en.html
[22] Razavi, A., Salimifar, M., Mostafavi, M., & Baki Heskooyi, M. (2014). Short-term impact of financial markets on the behavior of heavy Iranian crude oil prices. Quantitative Economics Quarterly, 11(2), 115-143. http://dx.doi.org/10.5539/mas.v10n3p45
[23] Shahbazi, K., & Salimian, S. (2015). Predicting oil prices using meta-analysis method. Journal of Energy Economics Studies, 11, 67-93. http://iiesj.ir/article-1-301-en.html
[24] Firouzi Jahantigh, F., & Dehghani, S. (2015). Application of genetic algorithm in optimization of neural network architecture and oil price prediction (GADDN). Applied Economic Studies of Iran (Applied Economic Studies), 5(20), 122-97. https://dorl.net/dor/20.1001.1.22519165.1397.9.35.8.1
[25] Rostami, M., Naqvipour, M., Holy Bayat, M. (2017). Oil market volatility model based on regime choice approach. Financial Engineering and Securities Management, 9(35), 179-196. https://dorl.net/dor/20.1001.1.22519165.1397.9.35.8.1D
[26] Behmiri, N. B., Manso, J. R. P. (2013). How crude oil consumption impacts on economic growth of Sub-Saharan Africa? Energy, 54, 74–83. https://doi.org/10.1016/j.energy.2013.02.052
[27] Gao, X., Fang, W., An, F., Wang, Y. (2017). Detecting method for crude oil price fluctuation mechanism under different periodic time series. Applied Energy, 192, 201–212. https://doi.org/10.1016/j.apenergy.2017.02.014
[28] Li, R., Hu, Y., Heng, J., Chen, X. (2021). A novel multiscale forecasting model for crude oil price time series. Technological Forecasting and Social Change, 173, 121181. https://doi.org/10.1016/j.techfore.2021.121181
[29] Nademi, A., Nademi, Y. (2018). Forecasting crude oil prices by a semiparametric Markov switching model: OPEC, WTI, and Brent cases. Energy Economics, 74, 757–766. https://doi.org/10.1016/j.eneco.2018.06.020
[30] Karasu, S. and Altan, A. (2022). Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization. Energy, 242, 122964. https://doi.org/10.1016/j.energy.2021.122964
Journal of Petroleum Geomechanics

Articles in Press, Accepted Manuscript
Available Online from 20 March 2024

Files

Share

How to cite

Statistics