The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner
Ufuk Çelik and
Alphanumeric Journal, 2017, vol. 5, issue 1, 45-54
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: Forecasting; Multilayer-Perceptron; Neural Networks; Time Series (search for similar items in EconPapers)
JEL-codes: C45 C63 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:anm:alpnmr:v:5:y:2017:i:1:p:45-54
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