VRBKA, Jaromír, Petr ŠULEŘ, Veronika MACHOVÁ a 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, roč. 12, č. 2, s. 178-193. ISSN 1805-9112. Dostupné z: 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 a 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, roč.~12, č.~2, s.~178-193. ISSN~1805-9112. Dostupné z: https://dx.doi.org/10.36708/Littera\_{}Scripta2019/2/0.
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