Journals / Alphanumeric Journal / 2017 / Cilt 5, Sayı 1, 2017

The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner

Yapay Sinir Ağları İle Kıymetli Maden Fiyatlarının RapidMiner İle Tahmin Edilmesi

Pages
45–54
Publication Date
2017-06-30
DOI
—
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AI Summary & Key Takeaways

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

The results show that error rates are accurate in order to foresee the market trends.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, an Artificial Neural Network study has been implemented to forecast the prediction of precious metals such as gold, silver, platinum and palladium prices by using RapidMiner data mining software.
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Key Findings & Data:The five performance measures; root mean squared error, absolute error, relative error, Spearman's Rho and Kendall’s Tau are utilized to evaluate artificial neural network model.
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Conclusion & Impact:The results show that error rates are accurate in order to foresee the market trends.
🔬Design:Modeling & Simulation
📍Scope:2010 Verileri
⚙️Variables:Zaman Serileri · Tahmin · Yapay Sinir Ağları

Abstract

In this paper, an Artificial Neural Network study has been implemented to forecast the prediction of precious metals such as gold, silver, platinum and palladium prices by using RapidMiner data mining software. The five performance measures; root mean squared error, absolute error, relative error, Spearman's Rho and Kendall’s Tau are utilized to evaluate artificial neural network model. This study concentrates on data which includes gold, silver, palladium, platinum, Brent Petrol, natural gas prices, 30 years’ bond, 10 years’ bond, 5 years’ bond, S&P 500, Nasdaq, Dow Jones, FTSE100, DAX, CAC40, SMI, NIKKEI, HANH, SENG and Euro/USD within the period of 4th of January 2010 to 14th of December 2015. The prices on the last quarter of 2015 is used for forecasting and validation. The results show that error rates are accurate in order to foresee the market trends.

Özet

Bu çalışmada, RapidMiner veri madenciliği yazılımı kullanılarak Yapay Sinir Ağları ile altın, gümüş, platin ve paladyum gibi kıymetli madenlerin fiyatlarının tahmin edilmesi gerçekleştirilmiştir. Yapay sinir ağlarını değerlendirmek için beş performans ölçütü; ortalama karesel hata, mutlak hata, göreceli hata, Spearman Rho ve Kendall Tau kullanılmıştır. Bu çalışma, 4 Ocak 2010 ile 14 Aralık 2015 tarihleri arasındaki altın, gümüş, platin, paladyum, Brent Petrol, doğal gaz, 30 yıllık bono, 10 yıllık bono, 5 yıllık bono, S&P 500, Nasdaq, Dow Jones, FTSE100, DAX, CAC40, SMI, NIKKEI, HANH, SEND ve Avro/Dolar rakamlarını içeren veriler üzerine odaklanmıştır. 2015 yılının son çeyreğindeki veriler tahmin ve doğrulama için kullanılmıştır. Sonuçlar, pazar tahminleri için hata oranlarının kabul edilebilir olduğunu göstermiştir.

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Cite This Article

Çelik, U., Başarır, C. (2017). The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner. Alphanumeric Journal, Cilt 5, Sayı 1, 2017, 45–54. https://ajindex.com/articles/63574

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References (16)

  1. Abramowitz, M. & Stegun, I. A. 1964. Handbook of mathematical functions: with formulas, graphs, and mathematical tables, Courier Corporation. Adrangi, B. & Chatrath, A. 2002. The Dynamics of Palladium and Platinum Prices. Computational Economics, 19, 179-195.
  2. Akgiray, V., Booth, G. G., Hatem, J. J. & Mustafa, C. 1991. Conditional Dependence in Precious Metal Prices. Financial Review, 26, 367-386.
  3. Arango Thomas, L., Arias, F. & Florez, L. 2012. Determinants of commodity prices. Applied Economics, 44, 135-145.
  4. Awokuse, T. O. & Yang, J. 2003. The informational role of commodity prices in formulating monetary policy: a reexamination. Economics Letters, 79, 219-224.
  5. Baffes, J. 2007. Oil spills on other commodities. Resources Policy, 32, 126-134.
  6. Batten, J. A., Ciner, C. & Lucey, B. M. 2010. The macroeconomic determinants of volatility in precious metals markets. Resources Policy, 35, 65-71.
  7. Beahm, D. 2008. Five Fundamentals Will Drive Gold Price Higher in 2008.
  8. Benli, Y. K. & Yildiz, A. 2015. 21) ALTIN FİYATININ ZAMAN SERİSİ YÖNTEMLERİ VE YAPAY SİNİR AĞLARI İLE ÖNGÖRÜSÜ. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 42. Caudill, M. 1987. Neural networks primer, part I. AI Expert, 2, 46-52.
  9. Chen, M.-H. 2010. Understanding world metals prices—Returns, volatility and diversification. Resources Policy, 35, 127-140.
  10. Chen, X. & Fang, Y. 2013. Enterprise systems in financial sector–an application in precious metal trading forecasting. Enterprise Information Systems, 7, 558-568.
  11. Hammoudeh, S. & Yuan, Y. 2008. Metal volatility in presence of oil and interest rate shocks. Energy Economics, 30, 606-620.
  12. Hyndman, R. J. & Athanasopoulos, G. 2014. Forecasting: principles and practice, OTexts. Hyndman, R. J. & Koehler, A. B. 2006. Another look at measures of forecast accuracy. International journal of forecasting, 22, 679-688.
  13. Jain, A. & Ghosh, S. 2013. Dynamics of global oil prices, exchange rate and precious metal prices in India. Resources Policy, 38, 88-93.
  14. Kendall, M. G. 1938. A new measure of rank correlation. Biometrika, 30, 81-93.
  15. Minsky, M., Minsky, M. L. & Papert, S. 1969. Perceptrons: An Introduction to Computational Geometry. Minsky, M. & Papert, S. 1987. Perceptrons - Expanded Edition: An Introduction to Computational Geometry. Morariu, N., Iancu, E. & Vlad, S. 2009. A neural network model for time series forecasting. Romanian journal of economic forecasting, 12, 213-223.
  16. Morris, R. G. 1999. D.O. Hebb: The Organization of Behavior, Wiley: New York; 1949. Brain