SHS Web Conf.
Volume 73, 2020Innovative Economic Symposium 2019 – Potential of Eurasian Economic Union (IES2019)
|Number of page(s)||12|
|Section||Potential of the Eurasian Economic Union|
|Published online||13 January 2020|
Considering seasonal fluctuations on balancing time series with the use of artificial neural networks when forecasting US imports from the PRC
Institute of Technology and Business, School of Expertness and Valuation, Okružní 517/10 37001, České Budějovice Czech Republic
* Corresponding author: firstname.lastname@example.org
The paper’s objective is to propose a particular methodology to be used to regard seasonal fluctuations on balancing time series while using artificial neural networks based on the example of imports from the People's Republic of China (PRC) to the USA (US). The difficulty of forecasting the volume of foreign trade is usually given by the limitations of many conventional forecasting models. For the improvement of forecasting it is necessary to propose an approach that would hybridize econometric models and artificial intelligence models. Data for an analysis to be conducted are available on the World Bank website, etc. Information on US imports from the PRC will be used. Each forecast is given by a certain degree of probability which it will be fulfilled with. Although it appeared before the experiment that there was no reason to include the categorical variable to reflect seasonal fluctuations of the USA imports from the PRC, the assumption was not correct. An additional variable, in the form of monthly value measurements, brought greater order and accuracy to the balanced time series.
© The Authors, published by EDP Sciences, 2020
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