Long-term prediction of the crude oil price using a new particle swarm optimization algorithm

Document Type : Original Article

Authors

1 Department of physics, Sirjan University of Technology, Sirjan, Iran.

2 Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran

3 Department of Mechanical Engineering, Sirjan University of Technology

4 Department of Mechanical Engineering, University of Politecnico di Milano, Milan, Italy.

5 Department of Mechanical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.

Abstract

Oil is one of the most precious source of energy for the world and has an important role in the global economy. Therefore, the long-term prediction of the crude oil price is an important issue in economy and industry especially in recent years. The purpose of this paper is introducing a new Particle Swarm Optimization (PSO) algorithm to forecast the oil prices. Indeed, the PSO is a population-based optimization method inspired by the flocking behavior of birds. Its original version suffers from tripping in local minima. Here, the PSO is enhanced utilizing a convergence operator, an adaptive inertia weight and linear acceleration coefficients. The numerical results of mathematical test functions, obtained by the proposed algorithm and other variants of the PSO elucidate that this new approach operates competently in terms of the convergence speed, global optimality and solution accuracy. Furthermore, the effective variables on the long-term crude oil price are regarded and utilized as input data to the algorithm. The objective function of the optimization process considered in this research study is the summation of the square of the difference between the actual and the predicted oil prices. Finally, the long-term crude oil prices are accurately forecasted by the proposed strategy which proves its reliability and competence.

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Main Subjects


1. S. Abosedra and H. Baghestani, “On the predictive accuracy of crude oil future prices,” Energy Policy, vol. 32, pp. 1389-1393, 2004.
2. J. Kennedy and R.C. Eberhart, “Particle swarm optimization,” in: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, vol. IV, pp. 1942–1948, 1995.
3. P.J. Angeline, “Using selection to improve particle swarm optimization,” in: IEEE Congress on Evolutionary Computation, Anchorage, AK, pp. 84–89, 1998.
4. M.J. Mahmoodabadi, “Epidemic model analyzed via particle swarm optimization based homotopy perturbation method,” Informatics in Medicine Unlocked, vol. 18, pp. 100293, 2020.
5. M.J. Mahmoodabadi and M. Taherkhorsandi, “Optimal robust design of sliding-mode control based on multi-objective particle swarm optimization for chaotic uncertain problems,” International Journal of Advanced Design and Manufacturing Technology, vol. 10, no. 3, pp. 115-126, 2017.
6. M.J. Mahmoodabadi, M.B. Salahshoor Mottaghi, and A. Mahmodinejad, “Optimum design of fuzzy controllers for nonlinear systems using multiobjective particle swarm optimization,” Journal of Vibration and Control, vol. 22, no. 3, pp. 769-783, 2016.
7. B. Junyou, “Stock forecasting using PSO-trained neural networks,” in: Proceedings of the Congress on Evolutionary Computation, pp. 2879-2885, 2007.
8. T. Abolhassani and A.M. Yaghobbi, “Stock price forecasting using PSOSVM,” 3rd International Conference on Advanced Computer Theory and Engineering, pp. 352-356, 2010.
9. E. Hadavandi, A. Ghanbari, and S. Abassian-Naghneh, “Developing a time series model based on Particle Swarm Optimization for gold price forecasting,” Third International Conference on business intelligence and financial Engineering, pp. 337-340, 2010.
10. E. Assareh, M.A. Behrang, M.R. Assari, and A. Ghanbarzadeh, “Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran,” Energy, vol. 35, pp. 5223-5229, 2010.
11. K. Bi, and T. Qiu, “An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method,” Chinese Journal of Chemical Engineering, vol. 27, pp. 1888-1894, 2019.
12. B. Han, and X. Bian, “A hybrid PSO-SVM-based model for determination of oil recovery factor in the low-permeability reservoir,” Petroleum, vol. 4, pp. 43-49, 2018.
13. M.E.A. Ben Seghier, B. Keshtegar, K. Fah Tee, T. Zayed, and N.T. Trung, “Prediction of maximum pitting corrosion depth in oil and gas pipelines,” Engineering Failure Analysis, vol. 112, pp. 104505, 2020.
14. H.F. Gong, Z.S. Chen, Q.X. Zhu, and Y.L. He, “A monte carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries,” Applied Energy, vol. 197, no. 1, pp. 405-415, 2017.
15. A. Ejraei Bakyani, H. Sahebi, M. M. Ghiasi, N. Mirjordavi, and A. Bahadori, “Prediction of CO2–oil molecular diffusion using adaptive neurofuzzy inference system and particle swarm optimization technique,” Fuel, vol. 181, no. 1, pp. 178-187, 2016.
16. K.O. Akande, T.O. Owolabi, S.O. Olatunji, and A.A. AbdulRaheem, “A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir,” Journal of Petroleum Science and Engineering, vol. 150, pp. 43-53, 2017.
17. K. Li, G. Zhou, Y. Yang, F. Li, and Z. Jiao, “A novel prediction method for favorable reservoir of oil field based on grey wolf optimizer and twin support vector machine,” Journal of Petroleum Science and Engineering, vol. 189, pp. 106952, 2020.
18. Y.Y. Zhang, H. Wei, Y.D. Yang, H.B. Zheng, and J. Jiao, “Forecasting of dissolved gases in oil-immersed transformers based upon wavelet LS-SVM regression and PSO with mutation,” Energy Procedia, vol. 104, pp. 38-43, 2016.
19. R. Mendes, J. Kennedy, and J. Neves, “The fully informed particle swarm: Simpler, maybe better,” IEEE Transaction on Evolutionary Computation, vol. 83, pp. 204–210, 2004.
20. J.J. Liang, and P.N. Suganthan, “Dynamic multi-swarm particle swarm optimizer,” in: Proceeding of Swarm Intelligence Symposium, pp. 124–129, 2005.
21. J.J. Liang, A.K. Qin, P.N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Transaction on Evolutionary Computation, vol. 103, pp. 281-295, 2006.
22. Z. Zhan, J. Zhang, Y. Li, and H.S. Chung, “Adaptive particle swarm optimization,” IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 39, no. 6, pp. 1362–1381, 2009.
23. A. Ratnaweera and S.K. Halgamuge, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficient,” IEEE Transactions on Evolutionary Computation, vol. 83, pp. 240–255, 2004.
24. W.D. Chang, “A multi-crossover genetic approach to multivariable PID controllers tuning,” Expert System with Applications, vol. 33, pp. 620–626, 2007.
25. www.eia.gov
26. www.inflationdata.com
27. M. Hojati, C.R. Bector, and K. Smimou, “A simple method for fuzzy linear regression,” Management science proceedings of ASAC meeting, pp. 31–42, 2001.
28. M. Hojati, C.R. Bector, and K. Smimou, A simple method for computation of fuzzy linear regression, European Journal of Operational Research, vol. 166, no. 1, pp. 172-184, 2005.
29. E.C. Ozelkan and L. Duckstein, “Multi-objective fuzzy regression: A general frame work,” Computers and Operations Research, vol. 27, no. 7–8, pp. 635–652, 2000.
30. A. Azadeh, M. Moghaddam, M. Khakzad and V. Ebrahimipour, “A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting,” Computers & Industrial Engineering, vol. 62, no. 2, pp. 421-430, 2012.