## 1 Introduction
For example, in the case when a bank gives a loan to an agricultural producer on the basis of the bank yield estimate made by artificial intelligence, it, in turn, affects the yield: the lower the bank estimate, the lower the amount of loans issued, which reduces the ability of agricultural producers to use technology to improve yields [4]. Based on the above reasoning, the need is obvious to improve the algorithms of studying the dynamics of economic phenomena using tools of modern methods of data analysis and statistical models. At the same time, statistical models, supplementing the possibilities of teaching methods with or without a teacher, will provide transparency of conclusions, their verification and validation, and facilitate application of created tools for analysis and forecasting not as \"black boxes\" but as understandable in a functioning mechanism. The described qualities are especially important in study of complex systems in which there may be elements of chaotic behavior. Sometimes, for such systems, understanding the logic of what is happening is no less important than the accuracy of the resulting forecast. However, such models are built on the basis of certain theoretical hypotheses and do not always allow to react quickly enough to changes in the trend, seasonal, and cyclical fluctuations to reveal hidden regularities in development of this particular phenomenon. In addition, the effectiveness of such models depends largely on the knowledge of the expert who creates them. One of the tools that can be used to highlight such changes in trends and identify hidden components is the singular spectrum analysis (SSA) method [5; 6; 7; 8; 9; 10]. This method allows us to decompose the original series of dynamics into basic components, which are a combination of a trend, cyclical fluctuations of different frequency, and random deviations. On the basis of the models built, it is possible to assess both the presence and properties of the main aspects of the dynamics and the relationship between the phenomena, as well as to build a forecast of the dynamics of the phenomena. However, although the SSA method can be used to build interpolation and extrapolation models, its efficiency for solving some economic problems is not high enough. As shown in [5], the SSA method has lower efficiency than, for example, neural networks when solving problems of electricity consumption forecasting. In this connection, a number of authors suggest combining SSA with other methods (first of all, with methods of intelligent data analysis) in order to improve the quality of extrapolation. The effectiveness of the combination technique is confirmed by the research in [5], where construction of a hybrid model in which the trend extracted by MSSA (multi-channel SSA) is processed by neural fuzzy network. Similarly, in [8], SSA was used to clean the training dataset from noise before training recurrent neural networks (RNN). When solving the problem of short-term net load forecasting, [9] proposes to use SSA as the method of preliminary dataset analysis, and the results obtained are extended with exogenous variables to obtain the best results of LSTM network usage. In some cases, such algorithms are less efficient compared to other hybrid algorithms; for example, when modeling the flow of tourists to Indonesia [10], the combination of SSA and fuzzy time series (FTS) generated worse results than the SARIMA-FTS combination. This can be explained by the fact that, in this study, SSA was used only to identify the trend and seasonal component while SARIMA also enables considering autoregression. From the point of view of considering the SSA method as a pre-processing tool, of interest is [11], in which the fuzzy C-means clustering (FCM) method was used before the SSA method, which enabled obtaining results in the FCM-SSA-ARIMA model that surpass the accuracy of SSA-ARIMA and ARIMA results.