mireabulletinRussian Technological JournalRussian Technological Journal2782-32102500-316XRTU MIREA10.32362/2500-316X-2020-8-4-7-45mireabulletin-229Research ArticleИНФОРМАЦИОННЫЕ СИСТЕМЫ. ИНФОРМАТИКА. ПРОБЛЕМЫ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИINFORMATION SYSTEMS. COMPUTER SCIENCES. ISSUES OF INFORMATION SECURITYОбзор современных моделей и методов анализа временных рядов динамики процессов в социальных, экономических и социотехнических системахReview of modern models and methods of analysis of time series of dynamics of processes in social, economic and socio-technical systemshttps://orcid.org/0000-0001-6418-6797АндриановаЕ. Г.AndrianovaE. G.

Андрианова Елена Гельевна, кандидат технических наук, доцент, доцент кафедры корпоративных информационных систем Института информационных технологий. Scopus Author ID: 57200555430

119454, Москва, пр-т Вернадского, д. 78

Elena G. Andrianova, Cand. Sci. (Engineering), Associated Professor, Associate Professor of the Department of ERP, IT Institute. Scopus Author ID: 57200555430

78, Vernadskogo pr., Moscow 119454

andrianova@mirea.ruГоловинС. А.GolovinS. A.

Головин Сергей Анатольевич, доктор технических наук, профессор, заведующий кафедрой математического обеспечения и стандартизации информационных технологий Института информационных технологий

119454, Москва, пр-т Вернадского, д. 78

Sergey A. Golovin, Dr. Sci. (Engineering), Professor, Head of Department of Mathematical Support and Standardization of Information Technologies, IT Institute

78, Vernadskogo pr., Moscow 119454

https://orcid.org/0000-0002-2115-5461ЗыковС. В.ZykovS. V.

Зыков Сергей Викторович, доктор технических наук, доцент, профессор Департамента программной инженерии факультета компьютерных наук. Scopus Author ID: 36146486900

101000, Россия, г. Москва, ул. Мясницкая, д. 20

Sergey V. Zykov, Dr. Sci. (Engineering), Associated Professor, Professor of School of Software Engineering of Faculty of Computer Science. Scopus Author ID: 36146486900

20, Myasnitskaya ul., Moscow 101000

https://orcid.org/0000-0002-6641-1609ЛеськоС. А.LeskoS. A.

Лесько Сергей Александрович, кандидат технических наук, доцент кафедры «Управление и моделирование систем» Института комплексной безопасности и специального приборостроения. Scopus Author ID: 57189664364

119454, Москва, пр-т Вернадского, д. 78

Sergey A. Lesko, Cand. Sci. (Engineering), Associate Professor of the Department «Management and Modeling of Systems», Institute of Integrated Security and Special Instrumentation. Scopus Author ID: 57189664364

78, Vernadskogo pr., Moscow 119454

ЧукалинаЕ. Р.ChukalinaE. R.

Чукалина Екатерина Романовна, магистрант кафедры корпоративных информационных систем Института информационных технологий

119454, Москва, пр-т Вернадского, д. 78

Ekaterina R. Chukalina, student, IT Institute

78, Vernadskogo pr., Moscow 119454

МИРЭА – Российский технологический университетРоссияMIREA – Russian Technological UniversityRussian FederationНИУ «Высшая школа экономики»РоссияNRU "Higher School of Economics"Russian Federation20200608202084745Copyright © Андрианова Е.Г., Головин С.А., Зыков С.В., Лесько С.А., Чукалина Е.Р., 20202020Андрианова Е.Г., Головин С.А., Зыков С.В., Лесько С.А., Чукалина Е.Р.Andrianova E.G., Golovin S.A., Zykov S.V., Lesko S.A., Chukalina E.R.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.rtj-mirea.ru/jour/article/view/229

Описаны направления перспективных исследований в области анализа и моделирования динамики временных рядов процессов в сложных системах с присутствием человеческого фактора. Динамика процессов в таких системах описывается нестационарными временными рядами. Прогнозирование эволюции подобных систем имеет большое значение для управления процессами в социальных (избирательные кампании), экономических (фондовые, фьючерсные и сырьевые рынки) и социотехнических системах (социальные сети). Приведены общие сведения о временных рядах и задачах их анализа. Рассмотрены современные методы анализа временных рядов для экономических процессов. Результаты показывают, что экономические процессы нельзя считать полностью случайными, так как они имеют тенденцию к самоорганизации и, кроме того, подвержены влиянию памяти о предыдущих состояниях. Выявлено, что одной из главных задач при моделировании процессов в социотехнических системах (например, социальных сетях) является разработка математического аппарата для приведения данных к единой шкале измерений. Проанализированы современные модели анализа и прогнозирования электоральных процессов на основе анализа временных рядов: структурные, опросные, гибридные. На основе проведенного анализа рассмотрены их достоинства и недостатки. В заключении подтвержден вывод, что для описания процессов в сложных системах с наличием человеческого фактора помимо традиционных факторов, необходимо разрабатывать и использовать методы и инструментальные средства для учета возможности самоорганизации человеческих групп и наличия памяти о предыдущих состояниях системы.

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.

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The authors declare that there are no conflicts of interest present.