Open Access
SHS Web Conf.
Volume 73, 2020
Innovative Economic Symposium 2019 – Potential of Eurasian Economic Union (IES2019)
Article Number 01025
Number of page(s) 16
Section Potential of the Eurasian Economic Union
Published online 13 January 2020
  1. N.K. Ahmed, A.F. Atiya, N.E. Gayar, H. El-Shishiny, An Empirical Comparison of Machine Learning Models for Time Series Forecasting. Econometric Reviews, 29 (5-6),594-621 (2010) [CrossRef] [Google Scholar]
  2. P. Gogas, T. Papadimitriou, A. Agrapetidou, Forecasting bank failures and stress testing: A machine learning approach. International Journal of Forecasting, 34(3), 440-455 (2018) [CrossRef] [Google Scholar]
  3. R. Milward, G.H. Popescu, K. Frajtová-Michalíková, Z. Musová, V. Machová, Sensing, smart, and sustainable technologies in Industry 4.0: Cyber-physical networks, machine data capturing systems, and digitized mass production. Economics, Management, and Financial Markets, 14 (3),37-43 (2019) [Google Scholar]
  4. H. Ghoddusi, G.G. Creamer, N. Rafizadeh, Machine learning in energy economics and finance: A review. Energy Economics, 81, 709-727 (2019) [CrossRef] [Google Scholar]
  5. X. Zhong, D. Enke, Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), (2019) [CrossRef] [Google Scholar]
  6. J. Horák, T. Krulický, Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company. SHS Web of Conferences: Innovative Economic Symposium 2018 - Milestones and Trends of World Economy</i>, 61 (2019) [Google Scholar]
  7. A.R. Samanpour, A. Ruegenberg, R. Ahlers, The Future of Machine Learning and Predictive Analytics. Digital Marketplaces Unleashed, Berlin, Heidelberg: Springer Berlin Heidelberg, 297-309 (2018) [Google Scholar]
  8. Y. Liu, T. Xie, Machine learning versus econometrics: prediction of box office. Applied Economics Letters, 26(2), 124-130 (2018) [CrossRef] [Google Scholar]
  9. R. Carbonneau, K. Laframboise, R. Vahidov, Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140-1154 (2018) [CrossRef] [Google Scholar]
  10. T. Klieštik, Models of autoregression conditional heteroskedasticity garch and arch as a tool for modeling the volatility of financial time series. Ekonomicko-manažerské spectrum, 7(1), 2-10 (2013) [Google Scholar]
  11. H. Pao, A comparison of neural network and multiple regression analysis in modeling capital structure. Expert Systems with Applications, 35(3), 720-727 (2008) [CrossRef] [Google Scholar]
  12. A.R. Sayadi, S.M.M. Tavassoli, M. Monjezi, M. Rezaei. Application of neural networks to predict net present value in mining projects. Arabian Journal of Geosciences, 7(3), 1067-1072 (2012) [CrossRef] [Google Scholar]
  13. Z. Rowland, J. Vrbka, Using artificial neural networks for prediction of key indicators of a company in global world. Proceedings of the 16th International Scientific Conference Globalization and Its Socio-Economic Consequences, pp. 1896-1903 (2016) [Google Scholar]
  14. M. Vochozka, J. Horák. Comparison of neural networks and regression time series in prediction of silver price development. 9th International Scientific Conference Company Diagnostics, Controlling and Logistics (2018) [Google Scholar]
  15. S. Cho, J. Kim, J.K. Bae. An integrative model with subject weight based on neural network learning for bankruptcy prediction. Expert Systems with Applications, 36(1), 403-410 (2009) [CrossRef] [Google Scholar]
  16. H. Li, L.Y. Hong, Q. Zhou, H.J. Yu, The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation. International Journal of Systems Science, 46(11), 2072-2086 (2013) [CrossRef] [Google Scholar]
  17. S. Balcaen, H. Ooghe, 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93 (2006) [CrossRef] [Google Scholar]
  18. M. Kováčová, T. Klieštik, P. Kubala, K. Valášková, M. Radisic, J. Borocki, Bankruptcy models: Verifying their validity as a predictor of corporate failure. Polish Journal of Management Studies, 18(1), 167-179 (2018) [Google Scholar]
  19. M. Crăciun, C. Raţiu, D. Bucerzan, A. Manolescu, Actuality of Bankruptcy Prediction Models used in Decision Support System. International Journal of Computers Communications, 8(3), 375-383 (2013) [CrossRef] [Google Scholar]
  20. T. Klieštik, J. Vrbka, Z. Rowland, Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis. Equilibrium-Quarterly Journal of Economics and Economic Policy, 13(3), 569-593 (2018) [Google Scholar]
  21. P.F. Pai, C.S. Lin. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497-505 (2005) [CrossRef] [Google Scholar]
  22. G. Mélard, J.M. Pasteels. Automatic ARIMA modeling including interventions, using time series expert software. International Journal of Forecasting, 16(4), 497508 (2000) [Google Scholar]
  23. J. Junttila, Structural breaks, ARIMA model and Finnish inflation forecasts. International Journal of Forecasting, 17(2), 203-230 (2001) [CrossRef] [Google Scholar]
  24. T. Krulický, Using Kohonen networks in the analysis of transport companies in the Czech Republic. SHS Web of Conferences: Innovative Economic Symposium <i>2018 – Milestones and Trends of World Economy, 61 (2019) [Google Scholar]
  25. P. Šuleř, Using Kohonen´s neural networks to identify the bankruptcy of enterprises: Case study based on construction companies in South Bohemian region. Proceedings of the 5th International conference Innovation Management, Entrepreneurship and Sustainability, pp. 985-995 (2017) [Google Scholar]
  26. C. Tuffell, P. Kráľ, A. Siekelová, J. Horák, Cyber-physical smart manufacturing systems: Sustainable industrial networks, cognitive automation, and data-centric business models. Economics, Management, and Financial Markets, 14(2), 58-63 (2019) [Google Scholar]
  27. K. Valášková, T. Klieštik, M. Mišánková, The role of fuzzy logic in decision making process. 2014 2</i>nd International Conference on Management Innovation and Business Innovation, 44, pp. 143-148 (2014) [Google Scholar]
  28. Z. Rowland, P. Šuleř, M. Vochozka, Comparison of neural networks and regression time series in estimating the Czech Republic and China trade balance. SHS Web of Conferences: Innovative Economic Symposium 2018 – Milestones and Trends of World Economy, 61 (2019) [Google Scholar]
  29. J. Weijin, X. Yuhui. A novel method for nonlinear time series forecasting of time-delay neural network. Wuhan University Journal of Natural Sciences, 11(5), 1357-1361 (2006) [CrossRef] [Google Scholar]
  30. J. Horák, P. Šuleř, J. Vrbka, Comparison of neural networks and regression time series when predicting the export development from the USA to PRC. Proceedings of 6th International Scientific Conference Contemporary Issues in Business, Management and Economics Engineering (2019) [Google Scholar]
  31. J. Vrbka, J, Z. Rowland, P. Šuleř, Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance. SHS Web of Conferences: Innovative Economic Symposium 2018 – Milestones and Trends of World Economy, 61 (2019) [Google Scholar]
  32. World Bank [online], Available at: ations=CN-US&start=2014 (2019) [Google Scholar]

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