Scenario forecasting based on digital smart models of dynamic processes

The article presents the results of research on the development of intelligent predictive models of dynamic processes in technical, industrial, natural and social systems for several time cycles ahead in conditions of limited uncertainty in changing process parameters. To build models, identification algorithms based on inductive knowledge are used, that is regularities extracted with the help of intellectual analysis methods from the data of the functioning of the process under study. When building models, scenarios for changing the state of processes are formed depending on the potential change in factors. Based on the scenarios, the formation of recommended control actions is carried out, both for automatic control systems and for decision support.