How to cite this paper
Lahmiri, S. (2013). Hybrid systems for Brent volatility data forecasting: A comparative study.Uncertain Supply Chain Management, 1(3), 145-152.
Refrences
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Chandra, R. & Zhang, M. (2012). Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing, 86, 116-123.
Chang, C.-L., McAleer, M. & Tansuchat, R. (2010). Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets. Energy Economics, 32(6), 1445-1455.
Chen, X. & Shen, C. (2013). Study on temperature error processing technique for fiber optic gyroscope. Optik, 124, 784-792.
Cheong, C.W. (2009). Modeling and forecasting crude oil markets using ARCH-type models. Energy Policy, 37(6), 2346-2355.
Christoffersen, P., Jacobs, K., Ornthanalai, C. & Wang, Y. (2008). Option valuation with long-run and short-run volatility components. Journal of Financial Economics, 90(3), 272-297.
Ding, Z., Granger, C.W.J. & Engle, R.F. (1993). A Long Memory Property of Stock Market Returns and a New Model. Journal of Empirical Finance, 1, 83-106.
Elman, J.L. (1990). Finding Structure in Time. Cognitive Science, 14(2), 179-211.
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Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley Professional.
Hamade, R.F., Ammouri, A.H. & Artail, H. (2012). Toward predicting the performance of novice CAD users based on their profiled technical attributes. Engineering Applications of Artificial Intelligence, 25, 628-639.
Hou, A. & Suardi, S. (2012). A nonparametric GARCH model of crude oil price return volatility. Energy Economics, 34(2), 618-626.
Huang, S.-Y., Tsaih, R.-H. & Lin, W.-Y. (2012). Unsupervised neural networks approach for understanding fraudulent financial reporting. Industrial Management & Data Systems, 112(2), 224-244.
Kelo, S. & Dudul, S. (2012). A wavelet Elman neural network for short-term electrical load prediction under the influence of temperature. Electrical Power and Energy Systems, 43, 1063-1071.
Lin, C.-H. (2013). Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive. Electrical Power and Energy Systems, 52, 143-160.
Narayan, P.K. & Narayan, S. (2007). Modeling oil price volatility. Energy Policy, 35(12), 6549-6553.
Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59, 347-370.
Rumelhart, D.E., Hinton, G.E. & Williams, R.J. (1986). Learning Representations by Back-Propagating Errors. Nature, 323, 533-536.
Solomon, S., Nguyen, H., Liebowitz, J. & Agresti, W. (2006). Using data mining to improve traffic safety programs. Industrial Management & Data Systems, 106(5), 621-643.
Wei, Y., Wang, Y. & Huang, D. (2010). Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Economics, 32(6), 1477-1484.
Zaghloul, W., Lee, S.M. & Trimi, S. (2009). Text classification: neural networks vs support vector machines. Industrial Management & Data Systems, 109(5), 708-717.
Zakoïan, J.-M. (1994). Threshold Heteroskedastic Models. Journal of Economic Dynamics and Control, 18, 931-955.
Zhao, J., Zhu, X.L, Wang, W. & Liu, Y, (2013). Extended Kalman filter-based Elman networks for industrial time series prediction with GPU acceleration. Neurocomputing, in press.
Zhou, C., et al., (2013). PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze River. Automation in Construction. In press.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307-327.
Box, E.G.P., Jenkins, G. & G.C. Reinsel, G.C. (1994). Time Series Analysis: Forecasting and Control. Third edition, Prentice-Hall.
Chandra, R. & Zhang, M. (2012). Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing, 86, 116-123.
Chang, C.-L., McAleer, M. & Tansuchat, R. (2010). Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets. Energy Economics, 32(6), 1445-1455.
Chen, X. & Shen, C. (2013). Study on temperature error processing technique for fiber optic gyroscope. Optik, 124, 784-792.
Cheong, C.W. (2009). Modeling and forecasting crude oil markets using ARCH-type models. Energy Policy, 37(6), 2346-2355.
Christoffersen, P., Jacobs, K., Ornthanalai, C. & Wang, Y. (2008). Option valuation with long-run and short-run volatility components. Journal of Financial Economics, 90(3), 272-297.
Ding, Z., Granger, C.W.J. & Engle, R.F. (1993). A Long Memory Property of Stock Market Returns and a New Model. Journal of Empirical Finance, 1, 83-106.
Elman, J.L. (1990). Finding Structure in Time. Cognitive Science, 14(2), 179-211.
Engle, R.F. & Lee, G.G.J. (1999). A permanent and transitory component model of stock return volatility. In: Engle, R., White, H. (Eds.), Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W.J. Granger. Oxford University Press, Oxford, 475-497.
Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley Professional.
Hamade, R.F., Ammouri, A.H. & Artail, H. (2012). Toward predicting the performance of novice CAD users based on their profiled technical attributes. Engineering Applications of Artificial Intelligence, 25, 628-639.
Hou, A. & Suardi, S. (2012). A nonparametric GARCH model of crude oil price return volatility. Energy Economics, 34(2), 618-626.
Huang, S.-Y., Tsaih, R.-H. & Lin, W.-Y. (2012). Unsupervised neural networks approach for understanding fraudulent financial reporting. Industrial Management & Data Systems, 112(2), 224-244.
Kelo, S. & Dudul, S. (2012). A wavelet Elman neural network for short-term electrical load prediction under the influence of temperature. Electrical Power and Energy Systems, 43, 1063-1071.
Lin, C.-H. (2013). Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive. Electrical Power and Energy Systems, 52, 143-160.
Narayan, P.K. & Narayan, S. (2007). Modeling oil price volatility. Energy Policy, 35(12), 6549-6553.
Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59, 347-370.
Rumelhart, D.E., Hinton, G.E. & Williams, R.J. (1986). Learning Representations by Back-Propagating Errors. Nature, 323, 533-536.
Solomon, S., Nguyen, H., Liebowitz, J. & Agresti, W. (2006). Using data mining to improve traffic safety programs. Industrial Management & Data Systems, 106(5), 621-643.
Wei, Y., Wang, Y. & Huang, D. (2010). Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Economics, 32(6), 1477-1484.
Zaghloul, W., Lee, S.M. & Trimi, S. (2009). Text classification: neural networks vs support vector machines. Industrial Management & Data Systems, 109(5), 708-717.
Zakoïan, J.-M. (1994). Threshold Heteroskedastic Models. Journal of Economic Dynamics and Control, 18, 931-955.
Zhao, J., Zhu, X.L, Wang, W. & Liu, Y, (2013). Extended Kalman filter-based Elman networks for industrial time series prediction with GPU acceleration. Neurocomputing, in press.
Zhou, C., et al., (2013). PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze River. Automation in Construction. In press.