文章基本信息

标题：Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance
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作者：Jaromír Vrbka ; Zuzana Rowland ; Petr Šuleř 等
期刊名称：SHS Web of Conferences
印刷版ISSN：2416-5182
电子版ISSN：2261-2424
出版年度：2019
卷号：61
页码：1-13
DOI：10.1051/shsconf/20196101031
语种：English
出版社：EDP Sciences
摘要：China, by GDP, is the second largest economic power, and hence also a key player in the field of international relations. As far as the EU is concerned, it is China's largest trading partner. From this point of view, it is clear that monitoring export and import development between these partners is essential. This paper therefore aims to compare two useful methods, namely the accuracy of time series alignment through regression analysis and artificial neural networks, to assess the evolution of the EU and the People's Republic of China trade balance. Data on the export and import trends of these two partners since 2000 have been used, and it is clear that the trade balance was completely different that year than it is now. The development over time is interesting. The most appropriate curve is selected from the linear regression, and from the neural networks three useful neural structures are selected. We also look at the prediction of future developments while taking into account seasonal fluctuations.
关键词：trade balance;export and import;linear regression;neural networks