Prediction of Socio-Economic Indicators of the Megapolis Development on the Basis of the Intellectual Forecasting Information System “SHM Horizon”

Olga Kitova, Ludmila Dyakonova and Victoria Savinova

MPRA Paper from University Library of Munich, Germany

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 multi-criteria ranking method.

Keywords: Regional economics; Forecasting; Socio-economic indicators; Hybrid models; Machine learning; Neural networks; Decision trees (search for similar items in EconPapers)
JEL-codes: C40 C45 (search for similar items in EconPapers)
Date: 2020-07-24, Revised 2020-11-19
New Economics Papers: this item is included in nep-big, nep-cis, nep-cmp, nep-for and nep-ore
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