SHS Web Conf.
Volume 73, 2020Innovative Economic Symposium 2019 – Potential of Eurasian Economic Union (IES2019)
|Number of page(s)||12|
|Section||Potential of the Eurasian Economic Union|
|Published online||13 January 2020|
Comparison of neural networks and regression time series in predicting export from Czech Republic into People´s Republic of China
1 University of Žilina, The Faculty of Operation and Economics of Transport and Communications, Univerzitná 8215/1, 01026 Žilina Slovakia
2 University of Economics, Faculty of Finance and Accounting, nám, W. Churchilla 1938/4, 13067 Prague Czech Republic
* Corresponding author: email@example.com
The People´s Republic of China is one of the largest, but also the most demanding markets in the world. The trade is limited by a number of barriers, strong competition and unusual environment for trades from other parts of the world. Despite those limitations, Czech exporters are able to establish themselves in the Chinese market, exporting mainly machines and vehicles. To predict future export trends is very difficult; however, these predictions can be crucial not only for individual exporters but also for the whole national economy. For predictions, economists use causal, intuitive or statistical methods. The objective of the contribution is to compare the accuracy of equalizing time series by means of regression analysis and artificial neural networks for a possible prediction of future export trends on the example of the Czech Republic export to the People´s Republic of China. For the purposes of analysis by means of statistical methods, the data obtained from monthly statements from the period starting from the year 2000 and ending in July 2018. First, a linear regression is carried out and subsequently, neural networks are used for regression. Finally, the results are compared. It appeared that in practice, mainly all retained neural networks are applicable. However, the first of them showed significant deviations within a very short period of time.
© The Authors, published by EDP Sciences, 2020
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