Kitova, Olga and Dyakonova, Ludmila and Savinova, Victoria (2020): Prediction of SocioEconomic Indicators of the Megapolis Development on the Basis of the Intellectual Forecasting Information System “SHM Horizon”.

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Abstract
The article describes a system of hybrid models ‘SGM Horizon’ as intellectual forecasting information system. The system of forecasting models includes a set of regression models and an expandable set of intelligent models, including artificial neural networks, decision trees, etc. Regression models include systems of regression equations that describe the behavior of forecast indicators of the development of the Russian economy in the system of national accounts. The functioning of the system of equations is determined by scenario conditions set by expert. For those indicators whose forecasts do not meet the requirements of quality and accuracy, intelligent models based on machine learning are used. Using the ‘SHM Horizon’ tools, predictive calculations were performed for a system of 30 indicators of the social sphere of the City of Moscow using hybrid models, and for8 indicators a significant increase in the quality and accuracy of the forecast was achieved with artificial neural network models. The process of models building requires considerable time, in this regard, the authors see the further development of the system in the application of the multicriteria ranking method.
Item Type:  MPRA Paper 

Original Title:  Prediction of SocioEconomic Indicators of the Megapolis Development on the Basis of the Intellectual Forecasting Information System “SHM Horizon” 
English Title:  Prediction of SocioEconomic Indicators of the Megapolis Development on the Basis of the Intellectual Forecasting Information System “SHM Horizon” 
Language:  English 
Keywords:  Regional economics, Forecasting, Socioeconomic indicators, Hybrid models, Machine learning, Neural networks, Decision trees 
Subjects:  C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C40  General C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C45  Neural Networks and Related Topics 
Item ID:  104234 
Depositing User:  Olga Kitova 
Date Deposited:  26 Nov 2020 13:07 
Last Modified:  26 Nov 2020 13:07 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/104234 