文章基本信息

标题：Comparison of neural networks and regression time series in estimating the Czech Republic and China trade balance
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作者：Zuzana Rowland ; Petr Šuleř ; Marek Vochozka 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/20196101023
语种：English
出版社：EDP Sciences
摘要：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.
关键词：neural networks;regression analysis;comparison;trade balance;prediction