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About the System of Hybrid Forecast Models for Regional Situational Centers

Наименование публикации:About the System of Hybrid Forecast Models for Regional Situational CentersАвторы:Китова О. В. , Дьяконова Л. П., Савинова В. М., Брускин С. Н., Данько Т. П.
Секерин В.Д.
Тематическая область:Информатика
Экономика и экономические науки
Вид публикации:Статья в журнале
Электронная публикация:НетЯзык издания:РусскийГод издания:2017Страна издания: Румыния Наименование журнала или сборника:Journal of Applied Economic SciencesНомер журнала (с указанием года):Vol. XII, Issue 6 (2017)Наименование издательства:European Research Center of Managerial Studies in Business AdministrationКод ISSN или ISBN:1843-6110Количество страниц:9Количество печатных листов:0,563Индексация:Scopus,
RePEc, CEEOL, EBSCO, SJR
Библиографическая ссылка:Kitova, O.V., Dyakonova, L.P., Savinova, V.M., Bruskin, S.N., Danko, T.P., Sekerin, V.D. 2017. About the system of hybrid forecast models for regional situational centers. Journal of Applied Economic Sciences, Volume XII, Fall 6(52): 1606–1614. Ссылка на публикацию: https://e.mail.ru/attachment/15124771230000000728/0;1Аннотация (реферат):

​An effective system of public strategic management is the basis for forming a competitive economy. There is a monitoring system implemented in the public management of the Russian Federation that ensures the successful implementation of strategic plans at all public management levels. The monitoring includes the situation analysis and the construction of shortterm development forecasts. The article presents a system of hybrid models and software tools for short-term forecasting of the socio-economic indicators of the Russian Federation. The system of hybrid forecast models includes a set of regression models and an expandable set of intelligent models, including artificial neural networks, decision trees, etc. Regression models represent systems of regression equations describing the behavior of the forecast indicators of the Russian economy development in the system of national accounts. The functioning of the equations system is determined by the specified expert scenario conditions. Indicators for which it is not possible to build qualitative forecasts based on the regression model are forecasted using a module of intelligent models. The authors' methodology of a hybrid approach to forecasting the indicators of the social and economic development of the Russian Federation is described to provide federal and regional levels of the Russian economy with the means of analysis and forecasting. The advantages of the system are its openness and adaptability. The system can be implemented in situational regional and municipal centers.


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