Open Access
SHS Web Conf.
Volume 71, 2019
Eurasia: Sustainable Development, Security, Cooperation – 2019
Article Number 01003
Number of page(s) 8
Section New Geopolitical Trends in Central Eurasia
Published online 25 November 2019
  1. M.K. Rafsanjani, M. Samareh, Chaotic time series prediction by artificial neural network. Journal of Computational Methods in Sciences and Engineering, 16(3), 599-615 (2016). [CrossRef] [Google Scholar]
  2. X. Wang, M. Hang, Improved extreme learning machine for multivariate time series online sequential prediction. Engineering Applications of Artificial Inteligence, 40, 28-36 (2015). [CrossRef] [Google Scholar]
  3. F. Fernandez-Navarro, M. A. de la Cruz, P.A. Gutierrez, A. Castano, C. Hervas-Martinez, Time series forecasting by recurrent product unit neural network. Neural Computing & Applications, 29(3), 779-791 (2018). [Google Scholar]
  4. R. Chandra, Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction. IEEE Transactions on Neural Networks and Learning Systems, 26(12), 3123-3136 (2015). [CrossRef] [Google Scholar]
  5. J. Vrbka, Z. Rowland, P. Šuleř, Comparison of neural networks and regression time series in estimating the Czech Republic and PRC trade balance. In J. Horák (Ed.), Innovative Economic Symposium 2018Milestones and Trends of World Economy (IES2018), SHS Web of Conferences, 61, 01031. Beijing, China (2019). [Google Scholar]
  6. Czech Statistical Office, Když se řekne zahraniční obchod [When you say foreign trade] [online], Available at: (2018). [Google Scholar]
  7. J. Gourdon, S. Monjon, S. Poncet, Trade policy and industrial policy in China: What motivates public authorities to apply restrictions on exports? China Economic Review, 40, 105-120 (2016). [CrossRef] [Google Scholar]
  8. V. Stehel, P. Šuleř, Foreign trade between China and the Czech Republic. Littera Scripta, 9(3), 84-95 (2016). [Google Scholar]
  9. T. De Castro, Z. Stuchlíková, China-V4 trade relations – A Czech perspective. In S. Mráz, K. Brocková (Eds.), Proccedings of International Scientific Conference on Current Trends and Perspectives in Development of China - V4 Trade and Investment, 12-14 March 2014, Bratislava (pp. 46-60). Bratislava: Vydavateľstvo EKONÓM (2014). [Google Scholar]
  10. P. Higgins, T. Zha, W. Zhong, Forecasting China‘s economic growth and inflation. China Economic Review, 41(C), 46-61 (2016). [CrossRef] [Google Scholar]
  11. F. Rodrigues, I. Markou, F.C. Pereira, Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach. Information Fusion, 49, 120-129 (2019). [CrossRef] [Google Scholar]
  12. P. Rostan, A. Rostan, The versatility of spectrum analysis for forecasting financial time series. Journal of Forecasting, 37(3), 327-339 (2018). [CrossRef] [Google Scholar]
  13. T. Klieštik, M. Mišánková, K. Valášková, L. Švábová, Bankruptcy prevention: New effort to reflect on legal and social changes. Science and Engineering Ethics, 24(2), 791-803 (2018). [Google Scholar]
  14. M. Vochozka, J. Horák, P. Šuleř, Equalizing seasonal time series using artificial neural networks in predicting the Euro-Yuan Exchange rate. Journal of Risk and Financial Management, 12(2), 76 (2019). DOI: 10.3390/jrfm12020076. [CrossRef] [Google Scholar]
  15. M. Tkáč, R. Verner, Artificial neural networks in business: Two decades of research. Applied Soft Computing, 38, 788-804 (2016) [CrossRef] [Google Scholar]
  16. H.T. Pao, A comparison of neural network and multiple regression analysis in modelling capital structure. Expert Systems with Applications, 35(3), 720-727 (2008). [CrossRef] [Google Scholar]
  17. E. Guresen, G. Kayakutlu, Definition of artificial neural networks with comparison to other networks. Procedia Computer Science, 3, 426-433 (2011). [CrossRef] [Google Scholar]
  18. H. Altun, A. Bilgil, B.C. Fidan, Treatment of multi-dimensional data to enhance neural network estimators in regression problems. Expert Systems with Applications, 32(2), 599-605 (2007). [CrossRef] [Google Scholar]
  19. V. Boguslauskas, R. Mileris, Estimation of credit risk by artificial neural networks models. Engineering Economics, 64(4), 7-14 (2009). [Google Scholar]
  20. Z. Rowland, J. Vrbka, Using artificial neural networks for prediction of key indicators of a company in global world. In T. Kliestik (Ed.), Proceedings of 16th International Scientific Conference on Globalization and its Socio-Economic Consequences, Rajecké Teplice, Slovakia (pp. 1896-1903). Zilina: GEORG (2016). [Google Scholar]
  21. D. Santin, On the approximation of production functions: A comparison of artificial neural networks frontiers and efficiency techniques. Applied Economics Letters, 15(8), 597-600 (2008). [CrossRef] [Google Scholar]
  22. V. Stehel, J. Vrbka, Z. Rowland, 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, 2016(2), 62-73 (2016). [Google Scholar]
  23. Y. H. Hu, J.N. Hwang, Handbook of neural network signal processing (CRC Press, Boca Raton, 2001). [Google Scholar]

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