Review of modern models and methods of analysis of time series of dynamics of processes in social, economic and socio-technical systems

The directions of perspective research in the field of analysis and modeling of the dynamics of time series of processes in complex systems with the presence of the human factor are described. The dynamics of processes in such systems is described by nonstationary time series. Predicting the evolution of such systems is of great importance for managing processes in social (election campaigns), economic (stock, futures and commodity markets) and socio-technical systems (social networks). The general information on time series and tasks of their analysis is given. Modern methods of time series analysis for economic processes are considered. The results show that economic processes cannot be considered completely random, since they tend to self-organize and, moreover, are subject to the influence of memory of previous states. It was revealed that one of the main tasks in modeling processes in sociotechnical systems (for example, social networks) is the development of a mathematical apparatus for bringing data to a single measurement scale. The modern models of analysis and forecasting of electoral processes based on the analysis of time series: structural, polling, hybrid. Based on the analysis, their advantages and disadvantages are considered. In conclusion, it was concluded that to describe processes in complex systems with the presence of the human factor, in addition to traditional factors, it is necessary to develop and use methods and tools to take into account the possibility of self-organization of human groups and the presence of memory about previous states of the system.

non-stationary time series, complex systems, analysis of economic and social processes, analysis of social networks, forecasting the evolution of time series, models and methods of analysis of time series, human factor, electoral processes, self-organization of the system

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