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Journals / Ege Akademik Bakış Dergisi / 2010 / Cilt: 10 - Sayı: 2

CRUDE OIL PRICE FORECASTING WITH ARTIFICIAL NEURAL NETWORKS

HAM PETROL FİYATLARININ YAPAY SİNİR AĞLARI İLE TAHMİNİ

Serkan TAŞTAN
Ferhan DEMİRKOPARAN
Oğuz KAYNAR
Pages
559–573
Publication Date
2010-05-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

So, classical time series analysis method ARIMA and MLP and RBF Neural Networks which are able to model complex relationships in data set and have a large part in time series analysis recently are used in this study

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Almost every sector in economy is connected with oil directly or indirectly.
📊
Key Findings & Data:Consequently, the changes on petrol industry, and thus, on petrol prices create various effects on both country and world economy by means of chaining reactions turning up.
💡
Conclusion & Impact:So, classical time series analysis method ARIMA and MLP and RBF Neural Networks which are able to model complex relationships in data set and have a large part in time series analysis recently are used in this study
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:YSA · MLP · RBF

Abstract

Almost every sector in economy is connected with oil directly or indirectly. Consequently, the changes on petrol industry, and thus, on petrol prices create various effects on both country and world economy by means of chaining reactions turning up. For making affective plans for the future about petrol industry which has a considerably unsteady and interactive structure because of its complex dynamics, straight and confidential predictions are needed. So, classical time series analysis method ARIMA and MLP and RBF Neural Networks which are able to model complex relationships in data set and have a large part in time series analysis recently are used in this study

Özet

Ekonomide hemen her sektör, doğrudan ya da dolaylı olarak petrole bağımlıdır. Bu nedenle petrol piyasasında ve dolayısıyla fiyatında ortaya çıkan değişiklikler, oluşturdukları zincirleme reaksiyonlar aracılığı ile hem ülke, hem de dünya ekonomisi üzerinde çeşitli etkiler yaratmaktadır. Karmaşık dinamiklerinden dolayı, oldukça değişken ve etkileşimli bir yapıya sahip petrol piyasasında geleceğe yönelik etkili planlar yapmak için doğru ve güvenilir tahminlere gereksinim vardır. Bu amaçla çalışmamızda ham petrol fiyatlarını tahmin etmek için klasik zaman serileri analiz yöntemlerinden ARIMA ile veri seti içerisindeki karmaşık ilişkileri başarıyla modelleyebilen son yıllarda zaman serisi analizinde sıkça yer alan MLP ve RBF yapay sinir ağları kullanılmıştır

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Cite This Article

TAŞTAN, S., DEMİRKOPARAN, F., KAYNAR, O. (2010). CRUDE OIL PRICE FORECASTING WITH ARTIFICIAL NEURAL NETWORKS. Ege Akademik Bakış Dergisi, Cilt: 10 - Sayı: 2, 559–573. https://ajindex.com/articles/752136

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References (16)

  1. Abosedra, S. ve Baghetani, H., “On The Predictive Accuracy of Crude Oil Futures Prices”, Energy Policy, Volume:32, 2004.
  2. Aklin, K. ve Atman, S., Küresel Petrol Stratejilerinin Jeopolitik Açıdan Dünya ve Türkiye Üzerindeki Etkileri, İstanbul:
  3. İstanbul Ticaret Odası, Yayın-No:2006-48,
  4. Alexandridis A., Livanis E, "Forecasting Crude Oil Prices Using Wavelet Neural Networks".In the proc. of 5th FSDET, Athens, Greece, 8 May, 2008.
  5. Amin-Naseri, M. R. ve Gharacheh, E. A.,“A hybrid artificial intelligence approach to monthly forecasting oil price time series”. Proceedings of EANN, 2007.
  6. Bernabe, A. ve diğerleri, “A Multi-Model Approach for Describing Crude Oil Price Dynamics”, Physica A, Volume:338, 2004.
  7. 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.
  8. 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.
  9. Cybenko G. (1989), “Approximation by superpositions of a sigmoidal function. Mathematical Control Signals Systems” 2: s.303–314.
  10. 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.
  11. Franses P. H.(1996),Periodicity And Stochastic Trends in Economic Time Series, Oxford University Press Frechtling D. C.(1996),
  12. 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.
  13. Harrald, P. G. ve Kamstra, M., “Evolving Artificial Neural Networks To Combine Financial Forecasts”, IEEE Transactions On Evolutionary Computation, Volume:1, No:1, 1997.
  14. 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.
  15. Hornik K. (1991), “Approximation capability of multilayer feedforward networks.”,Neural Networks 4: s.251–257.
  16. Kaboudan, M. A., “Compumetric Forecas