SHS Web Conf.
Volume 73, 2020Innovative Economic Symposium 2019 – Potential of Eurasian Economic Union (IES2019)
|Number of page(s)||14|
|Section||Potential of the Eurasian Economic Union|
|Published online||13 January 2020|
Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations
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 objective of the contribution is to introduce a methodology for considering seasonal fluctuations in equalizing time series using artificial neural networks on the example of the Czech Republic and the People´s Republic of China trade balance. The data available is the data on monthly balance for the period between January 2000 and July 2018, that is, 223 input data. The unit is Euro. The data for the analysis are available on the World Bank web pages etc. Regression analysis is carried out using artificial neural networks. There are two types on neural networks generated, multilayer perceptron networks (MLP) and radial basis function networks (RBF). In order to achieve the optimal result, two sets of neural structures are generated. There are generated a total of 10,000 neural structures, out of which only 5 with the best characteristics are retained. Finally, the results of both groups of retained neural networks are compared. The contribution this paper brings is the involvement of variables that are able to forecast a possible seasonal fluctuation in the time series development when using artificial neural networks. Moreover, neural networks have been identified that achieve slightly better results than other networks, specifically these are the neural networks 1. MLP 13-6-1 and 3. MLP 13-8-1.
© The Authors, published by EDP Sciences, 2020
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