OPEC Crude Oil Prices Prediction Based on ChaosTheory and GMDH-GA Algorithm

Abdollahzadeh, Sohrab; Behnia, Sohrab; Majdi, Fatemeh

doi:10.22050/pbr.2024.456830.1332

OPEC Crude Oil Prices Prediction Based on ChaosTheory and GMDH-GA Algorithm

Document Type : Original Article

Authors

Sohrab Abdollahzadeh ¹

Sohrab Behnia ²

Fatemeh Majdi ¹

¹ industrial engineering dept., Urmia University of Technology

² physics dept., Urmia University of Technology

https://doi.org/10.22050/pbr.2024.456830.1332

Abstract

The price of Crude Oil is Exposed to Various Factors That Cause Random, Sudden and Chaotic Price Fluctuations. Accurate Forecasting of Oil Prices Has a Central Impact on The Macro Economy. The aim of This Study is to Predict the Fluctuations of OPEC Crude Oil in The long-term Using Chaos Theory and GMDH-GA Algorithm. First, the Daily Oil Price Time Series is Decomposed by Wavelet Transformation. Then, Chaos is Tested Using Embedding Dimension, Lyapunov Power and GA tests. Finally, Time Series Noises are Reduced by Reconstructing the Wavelet Phase Space. Three Nonlinear Models, GMDH-GA model, GMDH-GA Wavelet Model, and GMDH-GA Extended Model, Were Used to Forecast Time Series. Although the Results Showed that All three Models are Favorable in Terms of Root Mean Square Error (RMSE) and Correlation Coefficient, But the Developed GMDH-GA Neural Network Model with low RMSE and High Correlation Coefficient is the Most Effective in Predicting the Daily Price of OPEC Crude Oil. has it.

Highlights

Crude oil price prediction using chaos theory;
Proving that the crude oil price time series is chaotic;
Converting crude oil time series to wavelets;

Keywords

OPEC Crude Oil price Forecast

GMDH-GA

Chaos Theory

Renyi Dimension

Correlation Dimension

Subjects

Oil and Gas Economics and Management

Azadeh, A., Mohsen Moghaddam, M., and Vahid E.(2012). A Flexible Neural Network-Fuzzy Mathematical Programming Algorithm for Improvement of Oil Price Estimation and Forecasting. Computers & Industrial Engineering, Vol. 62(2). 421–30.

Behradmehr, N., and Ahrari. M.(2014). Forecasting Crude Oil Prices: A Hybrid Model Based on Wavelet Transforms and Neural Networks. The International Journal of Humanities, Vol. 21(3). 131–50.

Bekheet, N.(2020). Developments of Oil Stocks and Their Impact on Oil Prices in the Global Energy Markets. International Journal of Psychosocial Rehabilitation, Vol. 24(7). 8885–93.

Bekiros, S., Hernandez, J. A., Salah Uddin, G., and Muzaffar. A. T.(2020). On the Predictability of Crude Oil Market: A Hybrid Multiscale Wavelet Approach. Journal of forecasting, 39(4). 599–614.

Casdagli, M., Eubank, S., Farmer, J. D., and Gibson, J.(1991). State Space Reconstruction in the Presence of Noise.” Physica D: Nonlinear Phenomena, Vol. 51(1–3). 52–98.

Cheng, F., Li, T., Wei, Y. M., and Fan, T.(2019). The VEC-NAR Model for Short-Term Forecasting of Oil Prices. Energy Economics, Vol. 78. 656–67.

Dong, Y., Jiang, H., Guo, Y., and Wang, J.(2024). A Novel Crude Oil Price Forecasting Model Using Decomposition and Deep Learning Networks. Engineering Applications of Artificial Intelligence, Vol. 133. 108111.

Fraser, A. M., and Swinney, H. L.(1986). Independent Coordinates for Strange Attractors from Mutual Information. Physical review A, Vol. 33(2). 1134.

Gencay, R., and Dechert, W. D.(992). An Algorithm for the n Lyapunov Exponents of an N-Dimensional Unknown Dynamical System. Physica D: Nonlinear Phenomena, Vol. 59(1–3). 142–57.

Ghoddusi, H., Creamer, G. G., and Rafizadeh, N.(2019). Machine Learning in Energy Economics and Finance: A Review. Energy Economics, Vol. 81. 709–27.

Guo, X., DaCheng Li, D., and Zhang, A.(2012). Improved Support Vector Machine Oil Price Forecast Model Based on Genetic Algorithm Optimization Parameters. Aasri Procedia, Vol. 1. 525–30.

Huang, Y., and Deng, Y.(2021). A New Crude Oil Price Forecasting Model Based on Variational Mode Decomposition. Knowledge-Based Systems, Vol. 213. 106669.

Iqbal, W., Fatima, A., Yumei, H., Abbas, Q., and Iram, R.(2020). Oil Supply Risk and Affecting Parameters Associated with Oil Supplementation and Disruption. Journal of Cleaner Production, Vol. 255. 120187.

Iram, H., Jabbar, A., and Bhatti, A. A.(2022). Commodity Price Shocks and Industrial Production of Pakistan. NICE Research Journal, 89–108.

Jabalameli, F., Ghorbani, P., and Ahmadian, M.(2020). Risk Management in Oil Market: A Comparison between Multivariate GARCH Models and Copula-Based Models. Iranian Economic Review, Vol. 24(2). 489–513.

Jiang, H., Hu,W., Xiao, L., and Dong. Y.(2022). A Decomposition Ensemble Based Deep Learning Approach for Crude Oil Price Forecasting. Resources Policy, Vol. 78. 102855.

Kaboudan, M. A.(2001). Compumetric Forecasting of Crude Oil Prices. In Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), IEEE, 283–87.

Kang, S. H., Cheong, C., and Yoon. S. M.(2011). Structural Changes and Volatility Transmission in Crude Oil Markets. Physica A: Statistical Mechanics and its Applications, Vol. 390(23–24). 4317–24.

Liu, W., and Chen, X.(2022). Natural Resources Commodity Prices Volatility and Economic Uncertainty: Evaluating the Role of Oil and Gas Rents in COVID-19. Resources Policy, Vol. 76. 102581.

Misiti, M., Yves Misiti, G. O., and Poggi, J. M.(1996). Wavelet Toolbox. The MathWorks Inc., Natick, MA, 15, 21.

Moshiri, S., and Foroutan F.(2004). Testing for Deterministic Chaos in Futures Crude Oil Price: Does Neural Network Lead to Better Forecast, Economics, Working Paper, 5.

Nguyen, H. T., and Nabney. I. T.(2008). Combining the Wavelet Transform and Forecasting Models to Predict Gas Forward Prices. In 2008 Seventh International Conference on Machine Learning and Applications, IEEE, 311–17.

Ott, E.(2002). Chaos in Dynamical Systems. Cambridge university press.

Pan, Z., Li Liu, X. H., and Huang. J.(2023). Geopolitical Uncertainty and Crude Oil Volatility: Evidence from Oil-Importing and Oil-Exporting Countries. Finance Research Letters, Vol. 52. 103565.

Panigrahy, C., Seal, A., Mahato,N. K., and Bhattacharjee. D.(2019). Differential Box Counting Methods for Estimating Fractal Dimension of Gray-Scale Images: A Survey. Chaos, Solitons & Fractals, Vol. 126. 178–202.

Percival, D. B., and Walden. A. T.(2000). 4 Wavelet Methods for Time Series Analysis. Cambridge university press.

Rosenstein, M. T., Collins, J. J., and De Luca. C. J.(1993). A Practical Method for Calculating Largest Lyapunov Exponents from Small Data Sets. Physica D: Nonlinear Phenomena, Vol. 65(1–2). 117–34.

Sivakumar, B.(2002). A Phase-Space Reconstruction Approach to Prediction of Suspended Sediment Concentration in Rivers. Journal of Hydrology, Vol. 258(1–4). 149–62.

Takens, F.(2006). Detecting Strange Attractors in Turbulence. In Dynamical Systems and Turbulence, Warwick 1980: Proceedings of a Symposium Held at the University of Warwick 1979/80, Springer, 366–81.

Uddin, G. S., Gençay, R., Bekiros, S., and Sahamkhadam, M.(2019). Enhancing the Predictability of Crude Oil Markets with Hybrid Wavelet Approaches. Economics letters, Vol. 182. 50–54.

Ullah, S., Chishti, M. Z., and Muhammad Majeed. T.(2020). The Asymmetric Effects of Oil Price Changes on Environmental Pollution: Evidence from the Top Ten Carbon Emitters. Environmental Science and Pollution Research, Vol. 27(23). 29623–35.

Ünal, H. T., and Başçiftçi, F.(2022). Evolutionary Design of Neural Network Architectures: A Review of Three Decades of Research. Artificial Intelligence Review, Vol. 55(3). 1723–1802.

Wu, Hanlin, P. L., Cao, J., and Xu, Z.(2024). Forecasting the Chinese Crude Oil Futures Volatility Using Jump Intensity and Markov-Regime Switching Model. Energy Economics, 107588.

Xie, W., Yu, L., Xu, S., and Wang, S.(2006). A New Method for Crude Oil Price Forecasting Based on Support Vector Machines. In Computational Science–ICCS 2006, 6th International Conference, Reading, UK, May 28–31, 2006, Proceedings, Part IV 6, Springer, 444–51.

Xiuzhen, X., Zheng, W., and Umair, M.(2022). Testing the Fluctuations of Oil Resource Price Volatility: A Hurdle for Economic Recovery. Resources Policy, Vol. 79. 102982.

Yu, L., Wang, S., and Lai, K. K.(2008). Forecasting Crude Oil Price with an EMD-Based Neural Network Ensemble Learning Paradigm. Energy economics, Vol. 30(5). 2623–35.

Yuan, H., Zhao, L., and Umair, M.(2023). Crude Oil Security in a Turbulent World: China’s Geopolitical Dilemmas and Opportunities. The Extractive Industries and Society, Vol. 16. 101334.

Zhang, J. L., Zhang, Y. J., and Zhang L.(2015). A Novel Hybrid Method for Crude Oil Price Forecasting. Energy Economics, Vol. 49. 649–59.

Zhang, S., Luo, J., Wang, S., and Liu, F.(2023). Oil Price Forecasting: A Hybrid GRU Neural Network Based on Decomposition–Reconstruction Methods. Expert Systems with Applications, Vol. 218. 119617.

Zhao, L., Cheng, L., Wan, Y., Zhang, H., and Zhang, Z.(2015). A VAR-SVM Model for Crude Oil Price Forecasting. International Journal of Global Energy Issues, Vol. 38(1–3). 126–44.