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

Rowland, Zuzana; Šuleř, Petr; Vochоzka, Marek

doi:10.1051/shsconf/20196101023

Cited by 12 publications

(8 citation statements)

References 7 publications

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“…Rowland, Šuleř and Vochozka [2], compared accuracy of time series by means of regression analysis and neural networks using trade balance data between CR and PRC in a certain period. They used statistical methods which gave them a basis to determine trade balance of the two countries.…”

Section: Review Of Related Literaturementioning

confidence: 99%

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

Vochоzka

Rowland

2020

SHS Web Conf.

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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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Section: Review Of Related Literaturementioning

confidence: 99%

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

Vochоzka

Rowland

2020

SHS Web Conf.

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“…The business relationship between the USA and China is currently supported by roughly 2.6 million jobs in a large number of industrial sectors including jobs which Chinese corporations have created in the USA [1]. Since Chinese middle class will go on developing in the next decade, American corporations will have the opportunity to benefit from a new and lucrative consumer basecamp which may further encourage the employment and economic growth [2].…”

Section: Introductionmentioning

confidence: 99%

Machine prediction of US imports from the PRC in the context of mutual sanctions

Šuleř

Mareček

2020

SHS Web Conf.

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The aim of this paper is to mechanically predict the import of the United States of America (USA) from the People's Republic of China (PRC). The trade restrictions of the USA and the PRC caused by the USA feeling of imbalance of trade between the two states have significantly influenced not only the trade between the two players, but also the overall climate of international trade. The result of this paper is the finding that multilayer perceptron networks (MLP) appear to be an excellent tool for predicting USA imports from the PRC. MLP networks can capture both the trend of the entire time series and its seasonal fluctuations. It also emerged that time series delays need to be applied. Acceptable results are shown to delay series of the order of 5 and 10 months. The mutual sanctions of both countries did not have a significant impact on the outcome of the machine learning prediction.

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“…Rowland, Šuleř and Vochozka [28] set out to compare the accuracy of time series equalizing using regression analysis and artificial neural networks on the example of the trade balance of China and the Czech Republic. Initially, they performed linear regression followed by regression using artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions

2020

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The USA decided to regulate the trade more by imposing tariffs on specific types of traded goods. It is therefore more interesting to find out whether the current technologies based on artificial intelligence with time series influenced by extraordinary factors such as the trade war between two powers are able to work. The objective of the contribution is to examine and subsequently equalize two time series – the USA import from the PRC and the USA export to the PRC. The dataset shows the course of the time series at monthly intervals between January 2000 and July 2019. 10,000 multilayer perceptron networks (MLP) are generated, out of which 5 with the best characteristics are retained. It has been proved that multilayer perceptron networks are a suitable tool for forecasting the development of the time series if there are no sudden fluctuations. Mutual sanctions of both states did not affect the result of machine learning forecasting.

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

Cited by 12 publications

References 7 publications

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

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

Machine prediction of US imports from the PRC in the context of mutual sanctions

Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions

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