___

• Abosedra, S. ve Baghetani, H., “On The Predictive Accuracy of Crude Oil Futures Prices”, Energy Policy, Volume:32, 2004.
• Aklin, K. ve Atman, S., Küresel Petrol Stratejilerinin Jeopolitik Açıdan Dünya ve Türkiye Üzerindeki Etkileri, İstanbul: İstanbul Ticaret Odası, Yayın-No:2006-48,
• Alexandridis A., Livanis E, "Forecasting Crude Oil Prices Using Wavelet Neural Networks".In the proc. of 5th FSDET, Athens, Greece, 8 May, 2008.
• Amin-Naseri, M. R. ve Gharacheh, E. A.,“A hybrid artificial intelligence approach to monthly forecasting oil price time series”. Proceedings of EANN, 2007.
• Bernabe, A. ve diğerleri, “A Multi-Model Approach for Describing Crude Oil Price Dynamics”, Physica A, Volume:338, 2004.
• Bianchini, M., Frasconi, P. & Gori, M. (1995) Learning without local minima in radial basis function networks. IEEE Trans. Neural Networks 6(3), s.749-755.
• Broomhead DS, Lowe D.(1988), “Multivariable functional interpolation and adaptive Networks”, Complex Systems 2: s.321–355.
• Chatfıeld C.,(2003),The analysis of time series: an introduction, CRC Pres
• Chen, S., Cowan, C. F. N., Grant, P. M. (1991), “Orthogonal least squares learning algorithm for radial basis function networks.”, IEEE Trans. Neural Networks 2(2), s.302-309.
• Cybenko G. (1989), “Approximation by superpositions of a sigmoidal function.
• Mathematical Control Signals Systems” 2: s.303–314. Fausett, L.(1994), Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall.
• Fernandez, V., “Forecasting commodity prices by classification methods: The cases of crude oil andnatural gas spot prices”, Banco Central De Chile Conference, July 27, 2007.
• Franses P. H.(1996),Periodicity And Stochastic Trends in Economic Time
• Series, Oxford University Press Frechtling D. C.(1996), Practical Tourism Forecasting, Elsevier
• Ghaffarı, A. ve Zare, S., “A Novel Algorithm For Prediction Of Crude Oil Price Variation Based On Soft Computing”, Energy Economics, Volume:31, 2009.
• Harrald, P. G. ve Kamstra, M., “Evolving Artificial Neural Networks To Combine Financial Forecasts”, IEEE Transactions On Evolutionary Computation, Volume:1, No:1, 1997.
• Haykin, S.( 1999),Neural Networks - A Comprehensive Foundation, Prentice Hall.
• Hornik K, Stinchcombe M, White H.(1989). “Multilayer feedforward networks are universal approximators.”, Neural Networks 2: s.359–366.
• Hornik K. (1991), “Approximation capability of multilayer feedforward networks.” ,Neural Networks 4: s.251–257.
• Kaboudan, M. A., “Compumetric Forecasting Of Crude Oil Prices”, Proceedings of The 2001 Congress on Evolutioanry Computation, Volume:1, 2001.
• Kulkarni, S. ve Haidar, I., “Forecasting Model For Crude Oil Price Using Artificial Neural Networks And Commodity Futures Prices”, International Journal of Computer Science and Information Security, Volume:2, No:1, 2009.
• Kuvulmaz J., Usanmaz S., Engin S. N.(2005), “Time-Series Forecasting by
• Means of Linear and Nonlinear Models.”, MICAI 2005, s.504-513
• Moody, J. & Darken, C. J. (1989), “Fast learning in networks of locally-tunes processing units.” ,Neural Computation 1(2), s.281-294.
• Öztemel, E.(2003),Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul.
• Pan, H., Haidar, I. ve Kulkarni, S., “Daily Prediction Of Short Term Trends Of Crude Oil Prices Using Neural Networks Exploiting Multimarket Dynamics”, Front. Comput. Sci., Volume:3, No:2, 2009.
• Park, J. ve Sandberg,I.W.(1991),” Universal approximations using Radial-Basis
• Function Network.”, Neural Computation 3(2), s.246-257
• Powell MJD.(1987),”Radial basis functions for multivariable interpolation: a review. In Algorithms for Approximation”, Mason JC, Cox MG (eds.) Carendon Press: Oxford; s.143–167.
• Tang, Z, Fishwick, P.A.(1993),”Feedforward neural nets as models for time series forecasting”,ORSA Journal on Computing, Vol. 5 (4), s.374–385.
• Uğurlu, E. ve Ünsal, A., “Ham Petrol İthalatı ve Ekonomik Büyüme: Türkiye”, Ekonometri ve İstatistik Sempozyumu‟na sunulan bildiri, Erzurum 27-29 Mayıs 2009.
• Xıe, W. ve diğerleri, "A New Method for Crude Oil Price Forecasting Based on Support Vector Machines", Lecture Notes in Computer Science, Volume:3994,
• Zhang, G., Patuwo, B. E. ve Hu, M. Y. (1998) “Forecasting with artificial neural networks: the state of the art”, International Journal of Forecasting, 14, s.35-62.