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@article{52381, author = {Rousek, Pavel and Mareček, Jan}, article_location = {Hradec Králové, Czech Republic}, article_number = {2}, keywords = {artificial neural networks; time series; export development; prediction; multilayer perceptron networks; radial basis function networks; seasonal fluctuations; United States; China}, language = {eng}, issn = {1804-7890}, journal = {Ad Alta: Journal of interdisciplinary research}, title = {Use of neural networks for predicting development of USA export to China taking into account time series seasonality}, volume = {9}, year = {2019} }
TY - JOUR ID - 52381 AU - Rousek, Pavel - Mareček, Jan PY - 2019 TI - Use of neural networks for predicting development of USA export to China taking into account time series seasonality JF - Ad Alta: Journal of interdisciplinary research VL - 9 IS - 2 SP - 299-304 EP - 299-304 PB - Magnanimitas SN - 18047890 KW - artificial neural networks KW - time series KW - export development KW - prediction KW - multilayer perceptron networks KW - radial basis function networks KW - seasonal fluctuations KW - United States KW - China N2 - The objective of the contribution is to propose a methodology of taking into consideration the seasonal fluctuations in time series equalization using artificial neural networks on the example of the United States of America export to the People´s Republic of China. It resulted that all retained structures are applicable, but the retained MLP networks of the B alternative achieve better results. It has been proven that with the use of artificial neural networks, it is possible to predict the export development efficiency and with a high degree of accuracy, especially in the short term and considering specific seasonal fluctuations. ER -
ROUSEK, Pavel a Jan MAREČEK. Use of neural networks for predicting development of USA export to China taking into account time series seasonality. \textit{Ad Alta: Journal of interdisciplinary research}. Hradec Králové, Czech Republic: Magnanimitas, roč.~9, č.~2, s.~299-304. ISSN~1804-7890. 2019.
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