| VRBKA, Jaromír, Petr ŠULEŘ, Veronika MACHOVÁ and Jakub HORÁK. Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People ́s Republic of China trade balance. Littera Scripta. České Budějovice: The Institute of Technology and Business in České Budějovice, 2019, vol. 12, No 2, p. 178-193. ISSN 1805-9112. Available from: https://dx.doi.org/10.36708/Littera_Scripta2019/2/0. |
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@article{52001,
author = {Vrbka, Jaromír and Šuleř, Petr and Machová, Veronika and Horák, Jakub},
article_location = {České Budějovice},
article_number = {2},
doi = {http://dx.doi.org/10.36708/Littera_Scripta2019/2/0},
keywords = {time series; artificial neural networks; trade balance; seasonal fluctuations; additional categorical variable; prediction},
language = {eng},
issn = {1805-9112},
journal = {Littera Scripta},
title = {Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People ́s Republic of China trade balance},
volume = {12},
year = {2019}
}
TY - JOUR
ID - 52001
AU - Vrbka, Jaromír - Šuleř, Petr - Machová, Veronika - Horák, Jakub
PY - 2019
TI - Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People ́s Republic of China trade balance
JF - Littera Scripta
VL - 12
IS - 2
SP - 178-193
EP - 178-193
PB - The Institute of Technology and Business in České Budějovice
SN - 18059112
KW - time series
KW - artificial neural networks
KW - trade balance
KW - seasonal fluctuations
KW - additional categorical variable
KW - prediction
N2 - The aim of the contribution is to propose a methodology for taking into account the seasonal fluctuations in equalizing time series by means of artificial neural networks on the example of the USA and People´s Republic of China trade balance. Regression by means of neural structures is carried out in two alternatives, where the second calculation takes into account the monthly seasonality of the time series. The result indicates that the additional variable in the form of the month in which the value was measured enables more order and accuracy.
ER -
VRBKA, Jaromír, Petr ŠULEŘ, Veronika MACHOVÁ and Jakub HORÁK. Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People ́s Republic of China trade balance. \textit{Littera Scripta}. České Budějovice: The Institute of Technology and Business in České Budějovice, 2019, vol.~12, No~2, p.~178-193. ISSN~1805-9112. Available from: https://dx.doi.org/10.36708/Littera\_{}Scripta2019/2/0.
|
