Yapay Sinir Ağları İle Kıymetli Maden Fiyatlarının RapidMiner İle Tahmin Edilmesi

Year 2017, , 45 - 54, 30.06.2017

Ufuk Çelik , Cağatay Başarır

https://doi.org/10.17093/alphanumeric.290381

Cited By: 18

Abstract

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.

Keywords

Zaman Serileri, Tahmin, Yapay Sinir Ağları, Çok Katmanlı Algılayıcı

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

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.

Keywords

Time-Series, Forecasting, Neural-Networks, Multilayer-Perceptron

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