Comparison of Neural Networks and Regression Time Series When Estimating the Copper Price Development

  • M. VochozkaEmail author
  • J. Horák
Part of the Contributions to Economics book series (CE)


In recent years, the primary copper ore stock has been cut sharply and the price of crude copper has been rising. On the other hand, thanks to a huge industrial interest, the production of copper products has increased significantly over recent years. It is therefore clear that the prediction of the copper price is very important. A variety of techniques, such as statistical methods—regression time series or artificial neural networks—are used for prediction. The aim of this contribution is to perform a regression analysis of the copper price development on the New York Stock Exchange using the mentioned linear regression and neural networks, expertly compare both methods, and identify the more suitable one for a possible prediction of future copper price developments. Input data includes copper price data from January 2006 to April 2018. First, linear regression is performed, and then, neural networks are used for regression analysis. A total of 1000 neuron structures are generated, five of which with the best characteristics are kept, and these are then further worked with. From the linear regression, the curve obtained by the spline function appears to be best, and the neural networks have all been proven to be usable in practice.


Copper Price development Price prediction Artificial neural networks Regression time series 


  1. Amanda Key: The copper price today: a brief overview. Investing news (2018). Accessed April 2018
  2. Augustin, A., Huilgol, P., Udupa, K.R., Bhat, U.K.: Effect of current density during electrodeposition on microstructure and hardness of textured Cu coating in the application of antimicrobial Al touch surface. J. Mech. Behav. Biomed. Mater. 63, 352–360 (2016)CrossRefGoogle Scholar
  3. Baral, A., Sarangi, C.K., Tripathy, B.C., Bhattacharya, I.N., Subbaiah, T.: Copper electrodeposition from sulfate solutions—effects of selenium. Hydrometallurgy 146, 8–14 (2014)CrossRefGoogle Scholar
  4. Boguslauskas, V., Mileris, R.: Estimation of credit risk by artificial neural networks model’s. Eng. Econ. 64(4), 7–14 (2009)Google Scholar
  5. Cloutier, M., Mantovani, D., Rosei, F.: Antibacterial coatings: challenges, perspectives, and opportunities. Trends Biotechnol. 33(11), 637–652 (2015)CrossRefGoogle Scholar
  6. Isa, N.N.C., Mohd, Y., Zaki, M.H.M., Mohamad, S.A.S.: Characterization of copper coating electrodeposited on stainless steel substrate. Int. J. Electrochem. Sci. 12, 6010–6021 (2017)Google Scholar
  7. Palza, H., Delgado, K., Curotto, N.: Synthesis of copper nanostructures on silica-based particles for antimicrobial organic coatings. Appl. Surf. Sci. 357, 86–90 (2015)CrossRefGoogle Scholar
  8. Rowland, Z., Vrbka, J.: Using artificial neural networks for prediction of key indicators of a company in global world. In: 16th International Scientific Conference on Globalization and its Socio-Economic Consequences, pp. 1896–1903. Ceske Budejovice, Czech Republic (2016)Google Scholar
  9. Santin, D.: On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques. Appl. Econ. Lett. 15(8), 597–600 (2008)CrossRefGoogle Scholar
  10. Singh, M.K., Gautam, R.K.: Mechanical and electrical behaviour of developed copper based hybrid composites. Mater. Today-Proc. India 5(2), 5692–5700 (2017)CrossRefGoogle Scholar
  11. Stehel, V., Vrbka, J., Rowland, Z.: Using neural networks for determining creditworthiness for the purpose of providing bank loan on the example of construction companies in South Region of Czech Republic. Ekonomicko-manažerské spektrum 2, 62–73 (2016)Google Scholar
  12. World Bank: Accessed April 2018 (2018)
  13. Zhuge, Q., Xu, L., Zhang, G.: LSTM neural network with emotional analysis for prediction of stock price. Eng. Lett. 25(2), 167–175 (2017)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Technology and Business in České BudějoviceČeské BudějoviceCzech Republic

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