EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 73 (2020) SHS Web Conf., 73 (2020) 01033 References
Open Access
Issue
SHS Web Conf.
Volume 73, 2020
Innovative Economic Symposium 2019 – Potential of Eurasian Economic Union (IES2019)
Article Number 01033
Number of page(s) 12
Section Potential of the Eurasian Economic Union
DOI https://doi.org/10.1051/shsconf/20207301033
Published online 13 January 2020
  1. L. Yu, S. Wang, K.K. Lai, Forecasting China’s Foreign Trade Volume with a Kernel-Based Hybrid Econometric-Ai Ensemble Learning Approach. Journal of Systems Science and Complexity, 21(1), 1-19 (2008) [CrossRef] [Google Scholar]
  2. A.B. Bernard, J.B. Jensen, Entry, Expansion, and Intensity in the US Export Boom, 1987-1992. Review of International Economics, 12(4), 662-675 (2004) [CrossRef] [Google Scholar]
  3. Z. Yu, J. Dong, Y. Feng, The Impacts of the Government Industrial Plans on China’s Exports and Trade Balance. Journal of Contemporary China, 20(3), 1-18 (2019) [Google Scholar]
  4. T.S. Chang, A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction. Expert Systems with Applications, 38(12), 14846-14851 (2011) [CrossRef] [Google Scholar]
  5. L. Yu, S. Wang, K.K. Lai, A hybrid econometric-AI ensemble learning model for Chinese foreign trade prediction. Computational Science - ICCS 2007 Proceedings, 4490, pp. 106+ (2007) [CrossRef] [Google Scholar]
  6. K. Valášková, T. Klieštik, L. Švábová, P. Adamko, Financial risk measurement and prediction modelling for sustainable development of business entities using regression analysis. Sustainability, 10(7), 2144 (2018) [CrossRef] [Google Scholar]
  7. V. Machová, J. Mareček, Estimation of the development of Czech Koruna to Chinese Yuan exchange rate using artificial neural networks. SHS Web of Conferences: Innovative Economic Symposium 2018: Milestones and Trends of World Economy, 61 (2019) [Google Scholar]
  8. S. Galeshchuk, The role of technological changes in foreign-exchange market inefficiency. Commemoration of the Birth Centennial of Herbert A. Simon 1916-2016 (Nobel Prize in Economics 1978), 27-34 (2016) [Google Scholar]
  9. Q. Zhang, Prediction on China’s merchandise exports based on BP neural network associated with sensitivity analysis. Modern Computer Science and Applications, pp. 351-356 (2016) [Google Scholar]
  10. 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, pp. 170-180 (2019) [Google Scholar]
  11. Ch. Bolton, V. Machová, M. Kováčová, K. Valášková, The power of human-machine collaboration: Artificial intelligence, business automation, and the smart economy. Economics. Management, and Financial Markets, 13(4), 51-56 (2018) [CrossRef] [Google Scholar]
  12. J. Vrbka, 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]
  13. 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]
  14. 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, 61 (2019) [Google Scholar]
  15. 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]
  16. T.S. Chang, A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction. Expert Systems with Applications, 38(12), 14846-14851 (2011) [CrossRef] [Google Scholar]
  17. 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]
  18. Y. Zhao, Y. Liu, X. Qiao, S. Wang, Z. Zhang, Y. Zhang, H. Li, Tracing value added in gross exports of China: Comparison with the USA, Japan, Korea, and India based on generalized LMDI. China Economic Review, 49(5), 24-44 (2018) [CrossRef] [Google Scholar]
  19. M. Kováčová, T. Klieštik, Logit and Probit application for the prediction of bankruptcy in Slovak companies. Equilibrium-Quarterly Journal of Economics and Economic Policy, 12(4), 775-791 (2017) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.