scholarly journals Comparison of neural networks and regression time series in estimating the Czech Republic and China trade balance

2019 ◽  
Vol 61 ◽  
pp. 01023 ◽  
Author(s):  
Zuzana Rowland ◽  
Petr Šuleř ◽  
Marek Vochozka
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Czech Republic ◽  
Linear Regression ◽  
Trade Balance ◽  
People’S Republic Of China ◽  
People's Republic Of China ◽  
The Czech Republic ◽  
Republic Of China ◽  
China Trade

Foreign trade has been and is considered to be very important. Trade balance measurement provides one of the best analyzes of a country's external economic relations. It serves as a monetary expression of economic transactions between a certain country and its foreign partners over a certain period. The aim of this paper is to compare the accuracy of time series alignment by means of regression analysis and neural networks on the example of the trade balance of the Czech Republic and the People's Republic of China. Trade balance data between the Czech Republic and the People's Republic of China is used. This is a monthly balance starting in 2000 and ending in July 2018. First, a linear regression is made followed by regression using artificial neural networks. A comparison of both methods at expert level and experience of the evaluator, the economist, is performed. Optically, the LOWESS curve appears to be best out of the linear regression and the fifth preserved RBF 1-24-1 network seems the mot appropriate out of neural networks.

scholarly journals Gender Motivation Differences of Czech and Chinese Employees

2019 ◽  
Vol 28 (1) ◽  
pp. 48-58
Author(s):  
Lenka Ližbetinová ◽  
Miloš Hitka
Keyword(s):  
Czech Republic ◽  
Descriptive Statistics ◽  
The Other ◽  
People’S Republic Of China ◽  
People's Republic Of China ◽  
The Czech Republic ◽  
Republic Of China ◽  
Incentive Programs ◽  
And Gender ◽  
Level Of Motivation

The aim of the article is to identify significant differences in motivational preferences of employees of Small and Medium-sized Enterprises (SMEs) by comparing their membership to the region and gender. The paper presents the partial outcomes of research on the level of motivation and the preferences of employees in the Czech Republic and the Beijing municipality administration of the People’s Republic of China. The survey was carried out in 2017 using a questionnaire. The questionnaires were distributed in small and medium-sized enterprises engaged in various areas of business. The sample included the entire territory of the Czech Republic (CR) and the Beijing municipality administration in the People’s Republic of China (PRC). A total of 2,673 respondents participated in the survey, of which 899 were respondents in the Beijing municipality administration and 1,774 respondents from the Czech Republic. Descriptive statistics was used to characterize the sampling unit. The other methods used to evaluate data in the article were the Student two-sample t-test, F test, and ANOVA. The differences in motivational preferences of employees revealed by the study can be used as a basis for creating appropriate incentive programs for multinational business teams.

scholarly journals Impact of China’s Growing Global Position on the Economy of the Czech Republic

2021 ◽  
Vol 92 ◽  
pp. 09017
Author(s):  
Kamila Veselá ◽  
Linda Pudilová
Keyword(s):  
Czech Republic ◽  
Growth Rates ◽  
Value Added ◽  
High Growth ◽  
Chinese Economy ◽  
People’S Republic Of China ◽  
People's Republic Of China ◽  
The Czech Republic ◽  
Republic Of China ◽  
Mutual Relations

Research background: The People’s Republic of China in the 21st century can be described as an economy with high growth rates and great ambitions. Some statistics even indicate that China will become the world’s new hegemon by 2040. The People’s Republic of China is not only one of the largest exporters but is increasingly speaking to the world economy and international relations. Since the beginning of the 1990s, mutual relations between the People’s Republic of China and the Czech Republic can be described as very good and constantly deepening, which can be evidenced, among other things, by the number of trade agreements. Purpose of the article: Purpose of the article is to evaluate a development of mutual relations between Czech and Chinese economies in order to predict their future development. Emphasis will be placed on the development of mutual trade through the evaluation of absolute and relative indicators and growth rates. Methods: The paper is based on secondary data from the database of the Czech Statistical Office. The key methods used in the article are the analysis of time series of real products of the Czech Republic and China and their foreign trade. The analysis focuses on the trend, deviations and development of absolute and relative indicators. Findings & Value added: The results of the analysis proved that the Chinese economy is growing on average more than twice as fast as the Czech economy. Because of that, the economic/living standards of the population of both countries are converging. The growth rate of the People’s Republic of China, together with its high spending on science and research, means that (if this trend is maintained) China is likely to become the world’s new hegemon in the near future.

scholarly journals Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance

2019 ◽  
Vol 61 ◽  
pp. 01031 ◽  
Author(s):  
Jaromír Vrbka ◽  
Zuzana Rowland ◽  
Petr Šuleř
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
International Relations ◽  
Trade Balance ◽  
Point Of View ◽  
Republic Of China ◽  
China Trade ◽  
The Neural Networks ◽  
Time Series Alignment ◽  
The Eu

China, by GDP, is the second largest economic power, and hence also a key player in the field of international relations. As far as the EU is concerned, it is China's largest trading partner. From this point of view, it is clear that monitoring export and import development between these partners is essential. This paper therefore aims to compare two useful methods, namely the accuracy of time series alignment through regression analysis and artificial neural networks, to assess the evolution of the EU and the People's Republic of China trade balance. Data on the export and import trends of these two partners since 2000 have been used, and it is clear that the trade balance was completely different that year than it is now. The development over time is interesting. The most appropriate curve is selected from the linear regression, and from the neural networks three useful neural structures are selected. We also look at the prediction of future developments while taking into account seasonal fluctuations.

scholarly journals Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations

2020 ◽  
Vol 73 ◽  
pp. 01032
Author(s):  
Marek Vochozka ◽  
Zuzana Rowland
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Czech Republic ◽  
Trade Balance ◽  
Seasonal Fluctuation ◽  
Seasonal Fluctuations ◽  
Republic Of China ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks

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.

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