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

@inproceedings{58381,
   author = {Rowland, Zuzana and Vrbka, Jaromír and Vochozka, Marek},
   address = {Les Ulis, France},
   booktitle = {SHS Web of Conferences: Innovative Economic Symposium - Potential of Eurasian Economic Union (IES)},
   edition = {73},
   editor = {Horák, J., Vrbka, J., Rowland, Z.},
   keywords = {forecasting; trade balance; machine learning; mutual sanctions; artificial neural networks},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Les Ulis, France},
   isbn = {978-2-7598-9094-1},
   pages = {nestránkováno},
   publisher = {EDP Sciences},
   title = {Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions},
   url = {https://www.shs-conferences.org/articles/shsconf/pdf/2020/01/shsconf_ies_2019_01025.pdf},
   year = {2020}
}

TY  - JOUR
ID  - 58381
AU  - Rowland, Zuzana - Vrbka, Jaromír - Vochozka, Marek
PY  - 2020
TI  - Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions
PB  - EDP Sciences
CY  - Les Ulis, France
SN  - 9782759890941
KW  - forecasting
KW  - trade balance
KW  - machine learning
KW  - mutual sanctions
KW  - artificial neural networks
UR  - https://www.shs-conferences.org/articles/shsconf/pdf/2020/01/shsconf_ies_2019_01025.pdf
N2  - 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.
ER  - 

ROWLAND, Zuzana, Jaromír VRBKA a Marek VOCHOZKA. Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions. In Horák, J., Vrbka, J., Rowland, Z. \textit{SHS Web of Conferences: Innovative Economic Symposium - Potential of Eurasian Economic Union (IES)}. 73. vyd. Les Ulis, France: EDP Sciences, 2020, s.~nestránkováno, 16 s. ISBN~978-2-7598-9094-1.