Abstract
In this paper, we investigated an ensemble neural network for the prediction of oil prices. Daily data from 1999 to 2012 were used to predict the West Taxes, Intermediate. Data were separated into four phases of training and testing using different percentages and obtained seven sub-datasets after implementing different attribute selection algorithms. We used three types of neural networks: Feed forward, Recurrent and Radial Basis Function networks. Finally a good ensemble neural network model is formulated by the weighted average method. Empirical results illustrated that the ensemble neural network outperformed other models.
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References
http://www.theforbiddenknowledge.com/../the_rothschild_bloodline.htm
Pindyck, R.S.: The dynamics of commodity spot and futures markets: a primer. The Energy Journal, 1–29 (2001)
Griffin, J.M.: OPEC behavior: a test of alternative hypotheses. The American Economic Review, 954–963 (1985)
Soytas, U., et al.: World oil prices, precious metal prices and macroeconomy in Turkey. Energy Policy 37(12), 5557–5566 (2009)
Maimon, O.Z., Rokach, L.: Data mining and knowledge discovery handbook, vol. 1. Springer (2005)
Abraham, A.: Artificial neural networks. In: Handbook of Measuring System Design (2005)
Kaboudan, M.: Compumetric forecasting of crude oil prices. In: Proceedings of the 2001 Congress on Evolutionary Computation. IEEE (2001)
Yu, L., Wang, S., Lai, K.K.: Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics 30(5), 2623–2635 (2008)
Haidar, I., Kulkarni, S., Pan, H.: Forecasting model for crude oil prices based on artificial neural networks. In: International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2008. IEEE (2008)
Alizadeh, A., Mafinezhad, K.: Monthly Brent oil price forecasting using artificial neural networks and a crisis index. In: 2010 International Conference on Electronics and Information Engineering (ICEIE). IEEE (2010)
Mingming, T., Jinliang, Z.: A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices. Journal of Economics and Business 64(4), 275–286 (2012)
Yu, L., Wang, S., Lai, K.K.: A generalized Intelligent-agent-based fuzzy group forecasting model for oil price prediction. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2008, IEEE (2008)
Reed, R.D., Marks, R.J.: Neural smithing: supervised learning in feedforward artificial neural networks. Mit Press (1998)
Chauvin, Y., Rumelhart, D.E.: Backpropagation: theory, architectures, and applications. Psychology Press (1995)
Demuth, H., Beale, M., Hagan, M.: Neural network toolboxTM 6. User’s guide (2008)
Maqsood, I., Khan, M.R., Abraham, A.: An ensemble of neural networks for weather forecasting. Neural Computing & Applications 13(2), 112–122 (2004)
Garner, S.R.: Weka: The waikato environment for knowledge analysis. In: Proceedings of the New Zealand Computer Science Research Students Conference. Citeseer (1995)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003)
Hall, M.A.: Correlation-based feature selection for machine learning. The University of Waikato (1999)
Robnik-Å ikonja, M., Kononenko, I.: An adaptation of Relief for attribute estimation in regression. In: Machine Learning: Proceedings of the Fourteenth International Conference, ICML 1997 (1997)
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Gabralla, L.A., Mahersia, H., Abraham, A. (2015). Ensemble Neurocomputing Based Oil Price Prediction. In: Abraham, A., Krömer, P., Snasel, V. (eds) Afro-European Conference for Industrial Advancement. Advances in Intelligent Systems and Computing, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-319-13572-4_24
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DOI: https://doi.org/10.1007/978-3-319-13572-4_24
Publisher Name: Springer, Cham
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