ROWLAND, Zuzana, Jaromír VRBKA and Marek VOCHOZKA. Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions. In Horák, J., Vrbka, J., Rowland, Z. SHS Web of Conferences: Innovative Economic Symposium - Potential of Eurasian Economic Union (IES). 73rd ed. Les Ulis, France: EDP Sciences, 2020. p. nestránkováno, 16 pp. ISBN 978-2-7598-9094-1.
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Basic information
Original name Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions
Authors ROWLAND, Zuzana (203 Czech Republic, guarantor, belonging to the institution), Jaromír VRBKA (203 Czech Republic, belonging to the institution) and Marek VOCHOZKA (203 Czech Republic, belonging to the institution).
Edition 73. vyd. Les Ulis, France, SHS Web of Conferences: Innovative Economic Symposium - Potential of Eurasian Economic Union (IES), p. nestránkováno, 16 pp. 2020.
Publisher EDP Sciences
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 50200 5.2 Economics and Business
Country of publisher France
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
Organization unit Institute of Technology and Business in České Budějovice
ISBN 978-2-7598-9094-1
UT WoS 000648964700025
Keywords in English forecasting; trade balance; machine learning; mutual sanctions; artificial neural networks
Tags BPE_MAE, RIV21, WOS
Changed by Changed by: Mgr. Nikola Vyhlidalová, učo 28324. Changed: 16/6/2021 08:20.
Abstract
Authors aim 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.
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