Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions

The USA decided to regulate the trade more by imposing tariffs on specific types of traded goods. It is therefore more interesting to find out whether the current technologies based on artificial intelligence with time series influenced by extraordinary factors such as the trade war between two powe...

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Main Authors:	Rowland Zuzana, Vrbka Jaromír, Vochozka Marek
Format:	Article
Language:	English
Published:	EDP Sciences 2020-01-01
Series:	SHS Web of Conferences
Subjects:	forecasting trade balance machine learning mutual sanctions artificial neural networks
Online Access:	https://www.shs-conferences.org/articles/shsconf/pdf/2020/01/shsconf_ies_2019_01025.pdf

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spelling	doaj-a56464963a5a4bdd918d1decf491d8ac2021-02-02T05:08:49ZengEDP SciencesSHS Web of Conferences2261-24242020-01-01730102510.1051/shsconf/20207301025shsconf_ies_2019_01025Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctionsRowland Zuzana0Vrbka Jaromír1Vochozka Marek2Institute of Technology and Business, School of Expertness and ValuationInstitute of Technology and Business, School of Expertness and ValuationInstitute of Technology and Business, School of Expertness and ValuationThe USA decided to regulate the trade more by imposing tariffs on specific types of traded goods. It is therefore more interesting to find out whether the current technologies based on artificial intelligence with time series influenced by extraordinary factors such as the trade war between two powers are able to work. The objective of the contribution is to examine and subsequently equalize two time series – the USA import from the PRC and the USA export to the PRC. The dataset shows the course of the time series at monthly intervals between January 2000 and July 2019. 10,000 multilayer perceptron networks (MLP) are generated, out of which 5 with the best characteristics are retained. It has been proved that multilayer perceptron networks are a suitable tool for forecasting the development of the time series if there are no sudden fluctuations. Mutual sanctions of both states did not affect the result of machine learning forecasting.https://www.shs-conferences.org/articles/shsconf/pdf/2020/01/shsconf_ies_2019_01025.pdfforecastingtrade balancemachine learningmutual sanctionsartificial neural networks
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Rowland Zuzana Vrbka Jaromír Vochozka Marek
spellingShingle	Rowland Zuzana Vrbka Jaromír Vochozka Marek Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions SHS Web of Conferences forecasting trade balance machine learning mutual sanctions artificial neural networks
author_facet	Rowland Zuzana Vrbka Jaromír Vochozka Marek
author_sort	Rowland Zuzana
title	Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions
title_short	Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions
title_full	Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions
title_fullStr	Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions
title_full_unstemmed	Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions
title_sort	machine learning forecasting of usa and prc balance of trade in context of mutual sanctions
publisher	EDP Sciences
series	SHS Web of Conferences
issn	2261-2424
publishDate	2020-01-01
description	The USA decided to regulate the trade more by imposing tariffs on specific types of traded goods. It is therefore more interesting to find out whether the current technologies based on artificial intelligence with time series influenced by extraordinary factors such as the trade war between two powers are able to work. The objective of the contribution is to examine and subsequently equalize two time series – the USA import from the PRC and the USA export to the PRC. The dataset shows the course of the time series at monthly intervals between January 2000 and July 2019. 10,000 multilayer perceptron networks (MLP) are generated, out of which 5 with the best characteristics are retained. It has been proved that multilayer perceptron networks are a suitable tool for forecasting the development of the time series if there are no sudden fluctuations. Mutual sanctions of both states did not affect the result of machine learning forecasting.
topic	forecasting trade balance machine learning mutual sanctions artificial neural networks
url	https://www.shs-conferences.org/articles/shsconf/pdf/2020/01/shsconf_ies_2019_01025.pdf
work_keys_str_mv	AT rowlandzuzana machinelearningforecastingofusaandprcbalanceoftradeincontextofmutualsanctions AT vrbkajaromir machinelearningforecastingofusaandprcbalanceoftradeincontextofmutualsanctions AT vochozkamarek machinelearningforecastingofusaandprcbalanceoftradeincontextofmutualsanctions
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Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions

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