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.
Palash S.V. [Analysis of the structural dynamics in the manufacturing industry at the national and regional levels]. Nauchno-tekhnicheskie vedomosti Sankt-Peterburgskogo gosudarstvennogo politekhnicheskogo universiteta. Ekonomicheskie nauki = Saint-Petersburg State Polytechnic University Journal. Economics, 2018, vol. 11, no. 1, pp. 64–76. (In Russ.) URL: Link
Pochukaeva O.V. [Innovative factors of Russian manufacturing industry]. Nauchnye trudy. Institut narodnokhozyaistvennogo prognozirovaniya RAN = Scientific Proceedings: Institute of National Economic Forecasting of RAS, 2012, iss. 10, pp. 257–279. URL: Link (In Russ.)
Podkorytov V.N. [Economic cycles: Theoretical conclusions or practical results?]. Izvestiya Ural'skogo gosudarstvennogo gornogo universiteta = News of the Ural State Mining University, 2014, no. 4, pp. 63–67. URL: Link (In Russ.)
Jambu M. Ierarkhicheskii klaster-analiz i sootvetstviya [Classification Automatique Pour L'Analyse des Données]. Moscow, Finansy i statistika Publ., 1988, 342 p.
Mandel I.D. Klasternyi analiz [Cluster Analysis]. Moscow, Finansy i statistika Publ., 1988, 176 p.
Coates A., Ng A.Y. Learning Feature Representations with K-means. In: Montavon G., Orr G.B., Müller K-R. Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science, vol. 7700. Springer-Verlag Berlin Heidelberg, 2012, pp. 561–580.
Baldin A.V., Borisevich V.B., Nesterenko V.I. [Factor and cluster analysis of the main indicators of production activity of transportation industry enterprises]. Rossiiskoe predprinimatel'stvo = Russian Journal of Entrepreneurship, 2006, no. 1, pp. 56–58. URL: Link (In Russ.)
Belashev B.Z., Dolgii K.A. [Application of global optimization in data clustering analysis]. Trudy Karel'skogo nauchnogo tsentra RAN. Ser.: Matematicheskoe modelirovanie i informatsionnye tekhnologii = Transactions of Karelian Research Centre of Russian Academy of Sciences. Mathematical Modeling and Information Technologies, 2015, no. 10, pp. 15–23. URL: Link (In Russ.)
Makarov V.L. [A review of mathematical models of innovation-driven economy]. Ekonomika i matematicheskie metody = Economics and Mathematical Methods, 2009, vol. 45, no. 1, pp. 3–14. (In Russ.)
Boldyrevskii P.B., Igoshev A.K., Kistanova L.A. [Analysis of the main factors of economic stability of industrial enterprises in Russia]. Vestnik Nizhegorodskogo universiteta im. N.I. Lobachevskogo. Ser.: Sotsial'nye nauki = Vestnik of Lobachevsky State University of Nizhni Novgorod. Social Sciences, 2018, no. 1, pp. 7–13. URL: Link (In Russ.)
Okun' A.S., Okun' S.A. [Neural network modeling as a tool for prediction]. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience, 2011, no. 33, pp. 45–52. URL: Link (In Russ.)
Kalaidin E.N., Dyudin M.S. [Neural Network Modeling of Exchange Dynamics]. Sovremennaya ekonomika: problemy i resheniya = Modern Economics: Problems and Solutions, 2012, no. 9, pp. 168–177. URL: Link (In Russ.)
Vasenkov D.V. [Methods of teaching artificial neural networks]. Komp'yuternye instrumenty v obrazovanii = Computer Tools in Education Journal, 2007, no. 1, pp. 20–29. URL: Link (In Russ.)
Semeikin V.D., Skupchenko A.V. [Modelling of Artificial Neural Networks in Matlab Environment]. Vestnik Astrakhanskogo gosudarstvennogo tekhnicheskogo universiteta. Ser.: Upravlenie, vychislitel'naya tekhnika i informatika = Vestnik of Astrakhan State Technical University. Series: Management, Computer Science and Informatics, 2009, no. 1, pp. 159–164. URL: Link (In Russ.)
Velichko E.P., Sokol'chik P.Yu. [Neural network controllers in the system position control]. Vestnik PNIPU. Khimicheskaya tekhnologiya i biotekhnologiya = PNRPU Bulletin. Chemical Technology and Biotechnology, 2015, no. 2, pp. 8–19. URL: Link (In Russ.)
Kallan R. Osnovnye kontseptsii neironnykh setei [Basic Concepts of Neural Networks]. Moscow, Vil'yams Publ., 2001, 287 p.
Fertsev A.A. [Neural network training acceleration using nvidiacuda technology for image recognition]. Vestnik Samarskogo gosudarstvennogo tekhnicheskogo universiteta. Ser.: Fiziko-matematicheskie nauki = Journal of Samara State Technical University, Series Physical and Mathematical Sciences, 2012, no. 1, pp. 183–191. (In Russ.) URL: Link
Osovskii S. Neironnye seti dlya obrabotki informatsii [Neural networks for information processing]. Moscow, Finansy i statistika Publ., 2002, 244 p.
MacKay D.J.C. A Practical Bayesian Framework for Backpropagation Networks. Neural Computation, 1992, vol. 4, no. 3, pp. 448–472. URL: Link
Nuzhnyi A.S., Shumskii S.A. [The Вayes regularization in the problem of function of many variables approximation]. Matematicheskoe modelirovanie = Mathematical Models and Computer Simulations, 2003, vol. 15, no. 9, pp. 55–63. URL: Link (In Russ.)
Grinchel' B.M., Nazarova E.A. Metody otsenki konkurentnoi privlekatel'nosti regionov: monografiya [Methods for assessing the competitive attractiveness of regions: a monograph]. St. Petersburg, SUAI Publ., 2014, 244 p.
