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The paper considered main applied aspects and methods of probabilistic forecasting of dynamic indicators when it is required to obtain an estimate of the probability of some event at a future time point associated with the selected indicator. At the same time, the main emphasis is made on the methods of binary forecasting and their interrelation with interval forecasting. It is shown that the problem of interval forecasting arises in many applied fields and is actual. The analysis of known probabilistic methods has shown that interval forecasting can be carried out on basis of the following methods: a) probabilistic regression methods; b) Bayesian methods; c) cluster methods; d) neural network methods; e) support vectors machine methods; f) random forests methods. The most promising methods for interval forecasting in system analysis are random forest methods and probabilistic support vectors machine methods. A detailed examination of each of these methods is beyond the scope of this paper, but the authors plan to consider these methods in detail in their future works.

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