Purpose – artificial neural networks are compared with mixed conclusions in terms of forecasting performance. The most researches indicate that deep-learning models are better than traditional statistical or mathematical models. The purpose of the article is to compare the accuracy of equalizing time series by means of regression analysis and neural networks on the example of the USA export to China. The aim is to show the possible uses and advantages of neural networks in practice.

Research methodology – the period for which the data (USA export to the PRC) are available is the monthly balance starting from January 1985 to August 2018. First of all, linear regression as the relatively simple mathematical method is carried out. Subsequently, neural networks as the computational models used in artificial intelligence are used for regression.

Findings – in terms of linear regression, the most suitable one appeared to be the curve obtained by means of the least squares methods by negative-exponential smoothing, and the curve obtained by means of the distance-weighted least squares method. In terms of neural networks, all retained structures appeared to be applicable in practice. Artificial neural networks have better representational power than traditional models.

Research limitations – the simplification (quite a significant one) appears both in the cases of linear regression and regression by means of neural networks. We work only with two variables – input variable (time) and output variable (USA export to the PRC).

Practical implications – in practice, the results – especially the method of artificial neural networks – can be used in the measurement and prediction of the development of exports, but especially in the short term. It can be stated that due to great simplification of the reality it isnʼt possible to predict extraordinary situations and their effect on the USA export to the PRC.

Originality/Value – the article focuses on the comparison of two statistical methods, in particular, artificial intelligence is not used in such applications. However, in many economic industries, it has proven better results. It is found that artificial neural networks are able to effectively learn dependencies in and between the time series in the form of export development data.