Open Access
Issue |
SHS Web Conf.
Volume 39, 2017
Innovative Economic Symposium 2017 (IES2017)
|
|
---|---|---|
Article Number | 01005 | |
Number of page(s) | 10 | |
Section | Strategic Partnerships in International Trade | |
DOI | https://doi.org/10.1051/shsconf/20173901005 | |
Published online | 06 December 2017 |
- R. Kriz, Nonlinear prediction of the GDP growth rate in the globalized world. Proceedings of the 16th International Scientific Conference of Globalization and its Cocio-Economic Consequences, (2016) [Google Scholar]
- D. Markovic, D. Petkovic, V. Nikolic, M. Milovancevic, Soft computing prediction of economic growth based in science and technology factor. A: Statistical Mechanics and its Applications, 465, 217–220 (2017) [CrossRef] [Google Scholar]
- M. M. A. Abdelaal, S. F. S. Mohamed, Statistical model of Egyptian economic growth prediction. Advances and Applications in Statistics, 47, 225–246 (2015) [CrossRef] [Google Scholar]
- N. Zhao, Y. Liu, G. Cao, E. L. Samson, J. Zhang, Forecasting China’s GDP at the pixel level using nighttime lights time series and population images. Giscience & Remote Sensing, 54(3), 407–425 (2017) [CrossRef] [Google Scholar]
- V. Jerabkova, Unemployment in the Czech Republic and its predictions based on the Box-Jenkins methodology. Proceedings of the 12th International Scientific Conference on Applications of Mathematics and Statistics in Economy, 189–196 (2009) [Google Scholar]
- P. Klimek, Practical use of the BoxJenkins methodology for seasonal financial data prediction. 7th International Scientific Conference on Finance and Performance of Firms in Science, Education and Practice, 598–611 (2015) [Google Scholar]
- M. Vochozka, Practical Comparison of Results of Statistic Regression Analysis and Neural Network Regression Analysis. Littera Scripta, 9(2), 156–168 (2016) [Google Scholar]
- S. Sokolov-Mladenovic, M. Milovancevic, I. Mladenovic, M. Alizamir, Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters. Computers in Human Behavior, 65, 43–45 (2016) [CrossRef] [Google Scholar]
- M. Stevanovic, S. Vujicic A. M. Gajic, Gross domestic product estimation based on electricity utilization by artificial neural network. Physica A: Statistical Mechanics and its Applications, 489, 28–31 (2018) [CrossRef] [Google Scholar]
- S. L. Ho, M. Xie a.T.N. Goh, A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction. Computers & Industrial Engineering, 42(2-4), 371–375 (2002) [CrossRef] [Google Scholar]
- H. Yip, H. Fan, Y. Chiang, Predicting the maintenance cost of construction equipment: Comparison between general regression neural network and Box-Jenkins time series models. Automation in Construction, 38, 30–38 (2014) [CrossRef] [Google Scholar]
- M. R. Gabor, L. A. Dorgo, Neural networks versus Box-Jenkins method for turnover forecasting: a case study on the romanian organisation. Transformation in Business & Economics, 16(1), 187–210 (2017) [Google Scholar]
- K. C. Lam, O. S. OshodI, Forecasting construction output: a comparison of artificial neural network and Box-Jenkins model. Engineering, Construction and Architectural Management, 23(3), 302–322 (2016) [Google Scholar]
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