Economic Analysis: Theory and Practice
 

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Cluster analysis and neural network modeling for movements of industrial production index of the Russian manufacturing industry

Vol. 18, Iss. 11, NOVEMBER 2019

Received: 16 September 2019

Received in revised form: 25 September 2019

Accepted: 11 October 2019

Available online: 29 November 2019

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: С02, С22, О13

Pages: 2158–2171

https://doi.org/10.24891/ea.18.11.2158

Boldyrevskii P.B. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
bpavel2@rambler.ru

ORCID id: not available

Igoshev A.K. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
akigoshev@iee.unn.ru

ORCID id: not available

Kistanova L.A. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
lakistanova@mail.ru

ORCID id: not available

Subject Ensuring the competitiveness and economic sustainability of Russian industrial undertakings necessitates improving of and searching for new forms and methods of assessment of all aspects of their activities. Development of multidimensional statistical methods is an important area to achieve the targets.
Objectives We focus on building mathematical models of spatial data and time series of the industrial production index enabling to analyze and forecast the development of certain sectors of the Russian economy.
Methods The study employs multidimensional statistical methods of cluster analysis and neural network modeling. The statistics of the Federal State Statistics Service from 1999 to 2016 served as the information base for modeling the changes in the manufacturing index.
Results We performed multidimensional classification of the subjects of the Russian Federation by the manufacturing index, using cluster analysis tools. The paper presents the methodology for analyzing and forecasting the industrial production index based on artificial neural networks. The average forecast error in the neural network modeling was at or below 0.08 percent.
Conclusions The classification revealed two clusters with different indicators of resistance to changes in external factors. We demonstrate the potential of the use of neural network modeling for projected values of the industrial production index for the subjects of the Russian Federation, and provide the obtained results.

Keywords: manufacturing, cluster analysis, neural networks

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