Abstract
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.
Highlights
The difficulty of forecasting the volume of foreign trade is usually given by the limitations of many conventional forecasting models
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 People's Republic of China (PRC), the assumption was not correct
In terms of analysing the relationship between the economic variables and foreign trade, this study proposes a new non-linear set of methodology for hybridizing non-linear econometric model and artificial neural networks (ANN) learning in order to forecast Chinese foreign trade
Summary
The difficulty of forecasting the volume of foreign trade is usually given by the limitations of many conventional forecasting models. In terms of analysing the relationship between the economic variables and foreign trade, this study proposes a new non-linear set of methodology for hybridizing non-linear econometric model and artificial neural networks (ANN) learning in order to forecast Chinese foreign trade. Rowland and Šuleř [12] compare the accuracy of equalizing time series by means of regression analysis and neural networks on the example of the trade balance between the EU and the Peoples Republic of China. Šuleř and Vochozka [13] aimed to compare the accuracy of equalizing time series by means of regression analysis and neural networks on the example of the trade balance between the Czech Republic and the Peoples Republic of China. With regard to foreign trade control, the impact of the key industries support policy on Chinas surplus decreases, which indicates that foreign controls of export to China have increased the impact of the key industries support policy on the trade balance
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