{"id":3523,"date":"2025-08-06T13:31:43","date_gmt":"2025-08-06T12:31:43","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=3523"},"modified":"2026-06-04T10:01:29","modified_gmt":"2026-06-04T09:01:29","slug":"27-3-6-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-3-6-en\/","title":{"rendered":"27.3.6 En"},"content":{"rendered":"\n

IAS development for industrial economic forecasting<\/strong><\/h2>\n\n\n\n

V.R. Iksanov<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/p>\n\n\n\n

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Abstract<\/em><\/strong>.<\/em> The ability to forecast trends for the future period has always been in demand in commercial and public enterprises. Based on forecasting, effective management decisions are made that contribute to the improvement of the enterprise and the country’s economy. Such calculations require a tool capable of making a high-quality calculation and analysis taking into account the volatile behavior of the market. To implement this system, it is necessary to consider macroeconomic indicators, industrial production indicators, and the right choice of software architecture. The purpose of the study is to analyze the software architecture and create an information and analytical system. Research methods<\/strong> \u2013 comparative analysis of software architecture and statistical classification. Results<\/strong>. Within the framework of this work, software architectures are analyzed to solve the problem of forecasting the economic indicators of the Russian Federation based on the author’s architecture assessment method. A comparative analysis table is compiled, the use of which the optimal architecture suitable for the problem is defined. The paper reveals the significance of the study, sets goals and objectives. The information-analytical forecasting system has been advanced and the system development grounded on the chosen architecture is presented. IAS operation is demonstrated, initial calculations by forecasting methods are made, and conclusions are drawn on the basis of the results. Each task was accomplished.<\/p>\n\n\n\n

Keywords<\/strong>:<\/em><\/strong> IAS, microservice architecture, IAS development, forecasting, industrial economics, MAPE, determination coefficient, Tkinter, Pandas<\/p>\n\n\n\n

For citation<\/strong>.<\/strong> Iksanov V.R. IAS development for industrial economic forecasting. News of the Kabardino-Balkarian Scientific Center of RAS.<\/em> 2025. Vol. 27. No. 3. Pp. 88\u201398. DOI: 10.35330\/1991-6639-2025-27-3-88-98<\/p>\n\n\n\n

R<\/strong>eferences<\/summary>\n
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  1. Kitova O.V., Savinova V.M., Iksanov V.R. Comparative analysis of machine learning methods for forecasting industrial indicators of the Russian Federation. Voprosy istorii [Questions of History]. 2022. No. 9-2. Pp. 248\u2013262. DOI: 10.31166\/VoprosyIstorii202209Statyi37. (In Russian)<\/li>\n\n\n\n
  2. Kitova O.V., Kolmakov I.B., Penkov I.A. Support vector machine method for forecasting investment indicators. Ekonomika, statistika i informatika. Vestnik UMO [Economics, Statistics and Informatics. Bulletin of UMO]. 2016. No. 4. Pp. 27\u201330. EDN: WHOQRX. (In Russian)<\/li>\n\n\n\n
  3. Baturin A.S. Time series and forecasting models [Electronic resource]. Access mode: https:\/\/4analytics.ru\/prognozirovanie\/vremennie-ryadi-i-modeli-prognozirovaniya.html (Accessed: 15.03.2025). (In Russian)<\/li>\n\n\n\n
  4. Kitova O.V., Savinova V.M., Dyakonova L.P. System of hybrid forecasting models for situational centers of regional government bodies and their application in education. Vestnik rossiyskogo ekonomicheskogo universiteta imeni G.V. Plekhanova [Bulletin of Plekhanov Russian University of Economics]. 2017. No. 5(95). Pp. 126\u2013134. EDN: ZSPYVB. (In Russian)<\/li>\n\n\n\n
  5. Kitova O.V., Kolmakov I.B., Domozhakov M.V. et al. Hybrid distributed regression and intelligent systems for forecasting indicators of socio-economic development of Russia. Vestnik rossiyskogo ekonomicheskogo universiteta imeni G.V. Plekhanova [Bulletin of Plekhanov Russian University of Economics]. 2017. No. 2(92). Pp. 147\u2013161. EDN: YNTSCD. (In Russian)<\/li>\n\n\n\n
  6. Savinova V.M. The system of econometric models for forecasting socio-economic indicators of the Russian Federation as the basis of the IAS \u201cHorizon\u201d. Modern Economy Success. 2022. No. 2. Pp. 140\u2013147. EDN: ULYEZO<\/li>\n\n\n\n
  7. Baturin A.S. Time series and forecasting models [Electronic resource]. Access mode: https:\/\/4analytics.ru\/prognozirovanie\/vremennie-ryadi-i-modeli-prognozirovaniya.html (Accessed: 11.03.2025). (In Russian)<\/li>\n\n\n\n
  8. Rustamov A.B. Forecast for the future of factors affecting the volume of production by the regional industrial entities in the digital economy. Ekonomika i predprinimatel’stvo [Economy and Entrepreneurship]. 2022. No. 4(141). Pp. 265\u2013272. DOI: 10.34925\/EIP.2022.141.4.050. EDN: BQXFDT<\/li>\n\n\n\n
  9. Tovma O.D. Main types of software architecture [Electronic resource]. Access mode: https:\/\/www.artofba.com\/post\/main-types-of-software-architecture-ru (Accessed: 03\/15\/2025). (In Russian)<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the author<\/strong><\/summary>\n
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    Vladislav R. Iksanov<\/strong>, Master, Assistant of the Department of Computer Science, Plekhanov Russian
    University of Economics;
    115054, Russia, Moscow, 36 Stremyannyy lane;
    vlad-iksanov@mail.ru, ORCID: https:\/\/orcid.org\/0009-0003-7810-3720, SPIN-code: 6750-3298<\/p>\n\n\n\n

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    IAS development for industrial economic forecasting V.R. Iksanov Abstract. The ability to forecast trends for the future period has always been in demand in commercial and public enterprises. Based on forecasting, effective management decisions are made that contribute to the improvement of the enterprise and the country’s economy. Such calculations require a tool capable of […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-3523","page","type-page","status-publish","hentry"],"yoast_head":"\n27.3.6 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-3-6-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"27.3.6 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"IAS development for industrial economic forecasting V.R. 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