YAPAY SİNİR AĞLARI YÖNTEMİ İLE PETROL FİYATLARI TAHMİNİ

Year 2018, Volume: 7 Issue: 3, 91 - 106, 29.09.2018

R.şebnem Ketrez Arif Saldanlı

Abstract

Petrol
fiyatlarındaki dalgalanmalar başta 1973 Petrol krizi olmak üzere birçok
ekonomik krizin temel nedeni olarak görülmektedir. Ekonominin arz ve talep
yanını aynı anda etkileyen bu faktörde meydana gelen fiyat hareketlerinin
öngörülmesi ve tahmin edilmesi önem arz etmektedir. Literatürde petrol
fiyatlarının yönünün kestirilmesinde farklı yöntemler kullanarak yapılmış
birçok çalışma bulunmaktadır. Bu çalışma kapsamında ise yapay sinir ağları
yönteminde farklı modeller kullanılarak petrol fiyatlarına ilişkin öngörü
başarıları incelenerek karşılaştırma yapmak amaçlanmıştır.

Keywords

Yapay Sinir Ağları, Fiyat Tahmini, Petrol Fiyatı, Enerji Piyasası

References

• Aghababaeyan, R., Siddiqui, T.& Khan, A.N. (2011) Forecasting the Tehran Stock Market by Artificial Neural Network. International Journal of Advanced Computer Science and Applications Special Issue on Artificial Intelligence.
• Amin-Naseri, M. R.,Gharacheh, E. A. (2007) A hybrid artificial intelligence approach to monthly forecasting of crude oil price time series., CEANN’2007, pp. 160-167.
• Azoff, M. (1994) Neural network time series forecasting of financial markets. New York: John Wiley.
• British Petrol. (2017) World Energy 2017 https://www.bp.com/content/dam /bp/en/corporate/pdf/energy-economics/statistical-review-2017/bp-statistical- review-of-world-energy-2017-full-report.pdf
• Diler, A. (2003) İMKB Ulusal-100 Endeksinin Yönünün Yapay Sinir Ağlarıyla Hata Geriye Yayma Yöntemi İle Tahmin Edilmesi. İMKB Dergisi (7)
• Elmas, Ç. (2011) Yapay Zeka Uygulamaları. Ankara, Seçkin Yayıncılık.
• Fauset, L. ( 2011) Fundemantals of Neural Networks
• Ghaffari, A., Zare S. (2009) A novel algorithm for prediction of crude oil price variation based on soft computing. Energy Economics 31, pp. 531-536.
• Haykin, S. (1999) Neural networks a comprehensive foundation. New Jersey: Wiley.
• Jammazi, R., Aloui, C. (2012) Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling. Energy Economics 34(3), pp. 828-841.
• Kaboudan, M. A. (2001). Compumetric forecasting of crude oil prices. pp. 283-287.
• McCulloch, W. S., Pitts. W. A. (1943) A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics 5, 115-33.
• McNelis, P. D., (2005) Neural Networks in Finance. London: Elsevier.
• Mirmirani, S., Li, H.C. (2004) A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil. Advances in Econometrics 19, pp. 203-223.
• Moshiri, S., Forotuan, F. (2006) Forecasting Nonlinear Crude Oil Future Prices. Energy Journal 27, pp. 83-97.
• Rast, M., (2001) Fuzzy neural networks for modelling commodity markets. (NAFIPS’2001), pp. 952-955.
• Rosenblatt, F., (1958) The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65, pp. 386-408.
• Shambora, W. E., Rossiter, R. (2007) Are there exploitable inefficiencies in the futures market for oil?. Energy Economics 29, pp. 18-27.
• Sugiyama, M., 2016. Introduction to Statistical Machine Learning. Massachusetts: Elsevier.
• Wang, S. Yu, L., Lai,. K.K. (2005) Crude oil price forecasting with TEI@I methodology. Journal of Systems Sciences and Complexity 18(2), pp. 145-166.
• Widrow, B., Hoff., M. E. (1960) Adaptive switching circuits. Institute of Radio Enginners, Wester Electronic Show and Convention, Convention Record, Part 4, pp. 96-104.
• Wongsinlatam, M. S. a. W. (2014) Prediction Model for Crude Oil Price Using Artificial Neural Networks. Applied Mathematical Sciences, Vol. 8, 2014, no. 80, pp. 3953 - 3965.
• Xie, W., Yu, L., Xu, S., Wang, S. (2006) A new method for crude oil price forecasting based on support vector machines. Lecture notes in computer science 3994, pp. 44-451.
• Xiong, T., Bao, Y., Hu, Z. (2013) Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step- Ahead Forecasting Models for Crude Oil Prices. Energy Economics, 40, 405-415.
• Zupan, J. (2003). Basics of artificial neural networks. Data handling in science and technology 23, 218.