Models for forecasting indicators based on neural network, regression analysis, and big data

Andrey Tuskov; Ivan Efimov; Botagoz S. Saparova; Altynai A. Saparova; Aigul A. Saparova

doi:10.1117/12.2669452

5 January 2023 Models for forecasting indicators based on neural network, regression analysis, and big data

Andrey Tuskov, Ivan Efimov, Botagoz S. Saparova, Altynai A. Saparova, Aigul A. Saparova

Proceedings Volume 12564, 2nd International Conference on Computer Applications for Management and Sustainable Development of Production and Industry (CMSD-II-2022); 1256404 (2023) https://doi.org/10.1117/12.2669452
Event: Computer Applications for Management and Sustainable Development of Production and Industry (CMSD2022), 2022, Dushanbe, Tajikistan

Abstract

The article discusses an approach to the analysis of static information based on the use of neural network technologies and regression models. The forecast for the neural network was made on the basis of official statistics, data on the development of advanced technologies, and also taking into account the current situation in the dynamics of the incidence in Russia for the period from 2003 to 2020. As a result, forecasts for the next 5 years were determined. Growth is expected to continue, but at a slower pace. At this stage, the incidence and the introduction of quarantine measures adversely affect the situation and lead to a reduction in consumption. It is worth noting that the trends in the production of innovative technologies will continue, despite the negative consequences of the pandemic. This is evidenced by forecast data.

Citation Download Citation

Andrey Tuskov, Ivan Efimov, Botagoz S. Saparova, Altynai A. Saparova, and Aigul A. Saparova "Models for forecasting indicators based on neural network, regression analysis, and big data", Proc. SPIE 12564, 2nd International Conference on Computer Applications for Management and Sustainable Development of Production and Industry (CMSD-II-2022), 1256404 (5 January 2023); https://doi.org/10.1117/12.2669452

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