Crude oil price forecasting: a biogeography-based optimization approach

References

• Al-Sasi, B. O., O. Taylan, and A. Demirbas. 2017. The impact of oil price volatility on economic growth. Energy Sources, Part B: Economics, Planning, and Policy 12:847–52. doi:10.1080/15567249.2017.1312641.
   Web of Science ®Google Scholar
• Arouri, M. E. H., A. Lahiani, A. Lévy, and D. K. Nguyen. 2012. Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models. Energy Economics 34:283–93. doi:10.1016/j.eneco.2011.10.015.
   Web of Science ®Google Scholar
• Barunk, J., and B. Malinsk. 2015. Forecasting the term structure of crude oil futures 420 prices with neural networks. Applied Energy 164:366–79. doi:10.1016/j.apenergy.2015.11.051.
   Web of Science ®Google Scholar
• Chai, J., Q. Lu, Y. Hu, S. Wang, K. K. Lai, and H. Liu. 2018. Analysis and Bayes statistical probability inference of crude oil price change point. Technological Forecasting & Social Change 126:271–83. doi:10.1016/j.techfore.2017.09.007.
   Web of Science ®Google Scholar
• Chen, Y., Y. Zou, Y. Zhou, and C. Zhang. 2016. Multi-step-ahead crude oil price forecasting based on grey wave forecasting method. Procedia Computer Science 91:1050–56. doi:10.1016/j.procs.2016.07.147.
   Google Scholar
• Chiromaa, H., S. Abdulkareema, and T. Herawanb. 2015. Evolutionary neural network model for West Texas Intermediate crude oil price prediction. Applied Energy 142:266–73. doi:10.1016/j.apenergy.2014.12.045.
   Web of Science ®Google Scholar
• Chuaykoblap, S., P. Chutima, A. Chandrachai, and N. Nupairoj. 2017. Expert-based text mining with Delphi method for crude oil price prediction. Industrial and Systems Engineering 25:545–63. doi:10.1504/IJISE.2017.083045.
   Google Scholar
• De Oliveira, F. A., C. N. Nobre, and L. E. Zárate. 2013. Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index- Case study of PETR4, Petrobras, Brazil. Expert Systems Applications 40:7596–606. doi:10.1016/j.eswa.2013.06.071.
   Web of Science ®Google Scholar
• Dehghani, H. 2017. Forecasting copper price using gene expression programming. Journal of Mining and Environment. doi:10.22044/jme.2017.6195.1435.
   Web of Science ®Google Scholar
• Dehghani, H., and D. Bogdanovic. 2017. Copper price estimation using bat algorithm. Resources Policy. doi:10.1016/j.resourpol.2017.10.015.
   Web of Science ®Google Scholar
• Demirbas, A., B. O. Al-Sasi, and A. S. Nizami. 2017. Recent volatility in the price of crude oil. Energy Sources, Part B: Economics, Planning, and Policy 12:408–14. doi:10.1080/15567249.2016.1153751.
   Web of Science ®Google Scholar
• Du, X., C. L. Yu, and D. J. Hayes. 2011. Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis. Energy Economics 33:497–503. doi:10.1016/j.eneco.2010.12.015.
   Web of Science ®Google Scholar
• Fan, L., S. Pan, Z. Li, and H. Li. 2016. An ica-based support vector regression 425 scheme for forecasting crude oil prices. Technological Forecasting and Social Change 112:245–53. doi:10.1016/j.techfore.2016.04.027.
   Web of Science ®Google Scholar
• Gong, X., and B. Lin. 2017. Forecasting the good and bad uncertainties of crude oil prices using a HAR framework. Energy Economics 67:315–27. doi:10.1016/j.eneco.2017.08.035.
   Web of Science ®Google Scholar
• Hadavandi, E., Ghanbari A., and Abbasian S. 2010. Developing a Time Series Model Based On Particle Swarm Optimization for Gold Price Forecasting. Third International Conference on Business Intelligence and Financial Engineering. 337–340.
   Google Scholar
• He, K., L. Yu, and K. K. Lai. 2012. Crude oil price analysis and forecasting using wavelet decomposed ensemble model. Energy 46:564–74. doi:10.1016/j.energy.2012.07.055.
   Web of Science ®Google Scholar
• Mussa, M., 2000. The impact of higher oil prices on the global economy. International Monetary Fund, 8.
   Google Scholar
• Jia, X., H. An, X. Sun, X. Huang, and X. Gao. 2016. Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach. Physica A: Statistical Mechanics and Its Applications 447:331–44. doi:10.1016/j.physa.2015.12.064.
   Web of Science ®Google Scholar
• Jianwei, E., Y. Bao, and J. Ye. 2017. Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis. Physica A: Statistical Mechanics and Its Applications 484:412–27. doi:10.1016/j.physa.2017.04.160.
   Web of Science ®Google Scholar
• Klein, T., and T. Walther. 2016. Oil price volatility forecast with mixture memory GARCH. Energy Economics 58:46–58. doi:10.1016/j.eneco.2016.06.004.
   Web of Science ®Google Scholar
• Kristjanpoller, W., and M. C. Minutolo. 2016. Forecasting volatility of oil price using an artificial neural network-GARCH model. Expert Systems With Applications 65:233–41. doi:10.1016/j.eswa.2016.08.045.
   Web of Science ®Google Scholar
• Lewis, C. D. 1982. Industrial and business forecasting methods: A practical guideto exponential smoothing and curve fitting. London and Boston: Butterworth Scientific.
   Google Scholar
• Luo, H., X. Liu, and S. Wang. 2017. Based on SARIMA-BP hybrid model and SSVM model of international crude oil price prediction research. ANZIAM Journal 58:143–61. doi:10.21914/anziamj.v58i0.10995.
   Google Scholar
• Ma, H., and D. Simon. 2017. Evolutionary computation with biogeography-based optimization. London, England:John Wiley & Sons, Inc.
   Google Scholar
• Mi, Z. F., Y. M. Wei, B. J. Tang, R. G. Cong, H. Yu, H. Cao, and D. Guan. 2017. Risk assessment of oil price from static and dynamic modelling approaches. Applied Economics 49:929–39. doi:10.1080/00036846.2016.1208359.
   Web of Science ®Google Scholar
• Mirjalili, S. A., S. M. Mirjalili, and A. Lewis. 2014. Let a biogeography-based optimizer train your multi-layer perceptron. Information Sciences 269:188–209. doi:10.1016/j.ins.2014.01.038.
   Web of Science ®Google Scholar
• Mostafaa, M., and A. El-Masry. 2016. Oil price forecasting using gene expression programming and artificial neural networks. Economic Modelling 54:40–53. doi:10.1016/j.econmod.2015.12.014.
   Web of Science ®Google Scholar
• Naser, H. 2016. Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach. Energy Economics 56:75–87. doi:10.1016/j.eneco.2016.02.017.
   Web of Science ®Google Scholar
• Sadorsky, P. 2002. Time-varying risk premiums in petroleum futures prices. Energy Economics 24 (6):539–56. doi:10.1016/S0140-9883(02)00062-2.
   Web of Science ®Google Scholar
• Shabri, A., and Samsudin, R. Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis. The Scientific World Journal. 2014.P.8. doi:10.1155/2014/854520.
   Google Scholar
• Simon, D. 2008. Biogeography-based optimization. IEEE Transactions Evolution Computation 12:702–13. doi:10.1109/TEVC.2008.919004.
   Web of Science ®Google Scholar
• Vasicek, O. 1977. An equilibrium characterization of the term structure. Journal of Financial Economics 5 (2):177–88. doi:10.1016/0304-405X(77)90016-2.
   Web of Science ®Google Scholar
• Wada, I., and G. Tuna. 2017. Crude oil price volatility and energy mix in Saudi Arabia. Energy Sources, Part B: Economics, Planning, and Policy 12:526–32. doi:10.1080/15567249.2016.1222027.
   Web of Science ®Google Scholar
• Yousefi, S., I. Weinreich, and D. Reinarz. 2005. Wavelet-based prediction of oil prices. Chaos, Solitons and Fractals 25:265–75. doi:10.1016/j.chaos.2004.11.015.
   Web of Science ®Google Scholar
• Yu, L., W. Dai, and L. Tang. 2015. A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting. Engineering Applications of Artificial Intelligence 47:110–21. doi:10.1016/j.engappai.2015.04.016.
   Web of Science ®Google Scholar
• Yu, L., S. Wang, and K. K. Lai. 2008. Forecasting crude oil price with an emd-based neural network ensemble learning paradigm. Energy Economics 30 (5):2623–35. doi:10.1016/j.eneco.2008.05.003.
   Web of Science ®Google Scholar
• Yu, L., H. Xu, and L. Tang. 2017. LSSVR ensemble learning with uncertain parameters for crude oil price forecasting. Applied Soft Computing 56:692–701. doi:10.1016/j.asoc.2016.09.023.
   Web of Science ®Google Scholar
• Zhu, B., and Y. Wei. 2013. Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology. Omega International Journal of Managed Sciences 41:517–24. doi:10.1016/j.omega.2012.06.005.
   Web of Science ®Google Scholar