SHS Web Conf.
Volume 73, 2020Innovative Economic Symposium 2019 – Potential of Eurasian Economic Union (IES2019)
|Number of page(s)||16|
|Section||Potential of the Eurasian Economic Union|
|Published online||13 January 2020|
Machine learning forecasting of CR and PRC balance of trade
1 University of Economics Faculty of Finance and Accounting, nám. W. Churchilla 1938/4, 13067, Prague, Czech Republic
2 Institute of Technology and Business School of Expertness and Valuation Okružní 517/10, 37001, České Budějovice, Czech Republic
* Corresponding author: email@example.com
International trade is an important factor of economic growth. While foreign trade has existed throughout the history, its political, economic and social importance has grown significantly in the last centuries. The objective of the contribution is to use machine learning forecasting for predicting the balance of trade of the Czech Republic (CR) and the People´s Republic of China (PRC) through analysing and machine learning forecasting of the CR import from the PRC and the CR export to the PRC. The data set includes monthly trade balance intervals from January 2000 to June 2019. The contribution investigates and subsequently smooths two time series: the CR import from the PRC; the CR export to the PRC. The balance of trade of both countries in the entire monitored period is negative from the perspective of the CR. A total of 10,000 neural networks are generated. 5 neural structures with the best characteristics are retained. Neural networks are able to capture both the trend of the entire time series and its seasonal fluctuations, but it is necessary to work with time series lag. The CR import from the PRC is growing and it is expected to grow in the future. The CR export to the PRC is growing and it is expected to grow in the future, but its increase in absolute values will be slower than the increase of the CR import from the PRC.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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