Prospective Models of Financial Forecasting of budget Revenues

The subject of the study is the choice of a model for financial forecasting of budget revenues, which allows the most correct assessment and obtaining a forecast value for the next period. The purpose of the study is to identify promising models of financial forecasting of budget revenues of the Russian Federation. DSGE models used since the 60s of the twentieth century have failed to identify a number of crises and timely predict the level of changes in government revenues in the United States, the Eurozone, and Russia, which did not allow for prompt adjustment of the policy pursued in the field of public revenue management. The novelty of the study consists in identifying the shortcomings of the modern methodology of financial forecasting associated with the obsolescence of the approaches used and the need to search for new models that allow you to quickly refine the prognostic results. The study used such methods as measuring predictive values and the size of their errors, analyzing and comparing the results obtained using methods and models of machine and deep learning. As a result of the study of predictive methods and models of machine and deep learning used in real business, the stock market and public finance, the most promising of them were selected. The main selection criteria were the possibility of modeling nonlinear relationships of parameters, the efficiency of calculation, the minimality of error, and the absence of a problem with retraining. In the course of the study, the expediency of time series decomposition was revealed, which made it possible to minimize predictive errors and choose the most accurate model for forecasting budget revenues of the Russian Federation. The results of the study can be used to form a system

of predictive indicators used to develop a dashboard system for civil servants in order to improve the accuracy and efficiency of their decisions.

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