| Issue |
SHS Web Conf.
Volume 61, 2019
Innovative Economic Symposium 2018 - Milestones and Trends of World Economy (IES2018)
|
|
|---|---|---|
| Article Number | 01023 | |
| Number of page(s) | 13 | |
| Section | Strategic Partnerships in International Trade | |
| DOI | https://doi.org/10.1051/shsconf/20196101023 | |
| Published online | 30 January 2019 | |
Comparison of neural networks and regression time series in estimating the Czech Republic and China trade balance
Institute of Technology and Business in České Budějovice, School of Expertness and Valuation, Okružní 517/10, 37001 České Budějovice, Czech Republic
* Corresponding author: rowland@mail.vstecb.cz
Foreign trade has been and is considered to be very important. Trade balance measurement provides one of the best analyzes of a country's external economic relations. It serves as a monetary expression of economic transactions between a certain country and its foreign partners over a certain period. The aim of this paper is to compare the accuracy of time series alignment by means of regression analysis and neural networks on the example of the trade balance of the Czech Republic and the People's Republic of China. Trade balance data between the Czech Republic and the People's Republic of China is used. This is a monthly balance starting in 2000 and ending in July 2018. First, a linear regression is made followed by regression using artificial neural networks. A comparison of both methods at expert level and experience of the evaluator, the economist, is performed. Optically, the LOWESS curve appears to be best out of the linear regression and the fifth preserved RBF 1-24-1 network seems the mot appropriate out of neural networks.
Key words: Neural networks / Regression analysis / Comparison / Trade balance / Prediction
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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