Artificial Intelligence in Forecasting Demographic Processes

Krayushkin, Stepan

doi:10.25673/115696

Proceedings of International Conference on Applied Innovation in IT · 2024/03/07 · Vol. 12 · Issue 1 · pp. 181–187

Artificial Intelligence in Forecasting Demographic Processes

Stepan Mezhov and Maxim Krayushkin

📄 Download PDF DOI: 10.25673/115696

Abstract

At the moment, there are no universal tools for forecasting indicators of socio-economic development in general and demographic in particular. However, the amount of budget allocations that are directed to solving personnel issues, creating social facilities and implementing other activities significant for economic, social and infrastructural development, development vectors depend on the forecast of demographic indicators by one-year-olds. In this article, a meaningful analysis is carried out an analysis of the researchers' work, based on the results of which it is determined that artificial intelligence models, in particular the most adaptive neural networks, are almost not used in predicting demographic indicators, forecasts based on models of artificial neural networks for all ages or by age groups, have almost no significance from the point of view of managing socio-economic development. The result of the research is a neural network methodological approach, methodology and tools for forecasting demographic processes for one-year-olds. The developed methodological approach to forecasting and tools are universal in the field of forecasting socio-economic indicators. In addition, the results described in the article can be used in other research in the field of forecasting, planning of any indicators.

Keywords

Forecasting Artificial Intelligence Models Artificial Neural Networks Model Error Population Size One-Year Old

References

J. Graunt, “Natural and political observations listed in the attached table of contents and made on the basis of bulletins on mortality. In relation to governance, religion, trade, growth, air, diseases and other changes of the named city. The work of John Graunt, citizen of London,” London, 1662.
T.R. Malthus, “Experience of the law on population,” L. : Petrokom, 1793, 292 p.
A. G. Volkov, “Pearl Raymond Demography,” M. : Soviet Encyclopedia, 1975, p. 556. (The Great Soviet Encyclopedia, ch. ed. A.M. Prokhorov, vol. 19. pp. 1969-1978).
P.K. Whelpton, “An empirical method of calculating future population,” Journal of the American Statistical Association, 1936, no. 31, pp. 457-473.
A.G. Vishnevsky, “Prospects for the development of Russia: the role of the demographic factor,” M.: Russian National Library, 2003, 51 p.
D. Meadows, “Limits of growth, 30 years later,” translated from English, M.: Akademkniga, 2007, 342 p.
H.A. Simon, “Rationality as a process and product of thinking,” translated from English by K. B. Kozlova and M. A. Blanco, [Online]. Available: https://www2.econ.iastate.edu/tesfatsi/RationalityAsProcessAndProductOfThought.ElyLecture1978.Hsimon.pdf.
J. Forrester, “World dynamics,” translated from English, M.: AST; St. Petersburg: Terra Fantastica, 2003, 379 p.
J. Forrester, “Dynamics of city development,” translated from English, M.: Progress, 1974, 281 p.
V.A. Tsybatov, “Macroeconomic modeling, forecasting and planning of regional development: a textbook,” Samara: Publishing House of Samara State University, 2016, 316 p.
Federal State Statistics Service: Official website, [Online]. Available: https://rosstat.gov.ru/folder/12781, [Accessed: 12 Jan 2024].
The BFGS method or one of the most effective optimization methods, [Online]. Available: https://habr.com/ru/post/333356, [Accessed: 04 Dec 2023].
S. Pattanayak, “Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python,” Apress, 2019, 480 p.
S. Lutfiani, M. Sri, and R. Hendrisman, “Forecasting the mortality rates of Indonesian population by using neural network,” International Conference on Mathematics: Pure, Applied and Computation, IOP Conf. Series: Journal of Physics: Conf. Series, pp. 974-012030, 2018.
N. Bheemanna and M.N. Megeri, “Prediction of India’s demographic and economic variables using the neural network auto-regression model,” International Journal of Statistics and Applied Mathematics, vol. 11, pp. 44-55.
A.A. Khafalyan, “Industrial statistics. Quality control, process analysis, experiment planning in the STATISTICA package: textbook,” M.: Rusains Publishing House, 2019, 384 p.
T. Theophili, “Deep learning for search engines: textbook,” 2020, 318 p. .
O. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems: textbook,” Jupiter 2020, 688 p. .
S. Haikin, “Neural Networks: textbook,” 2019, 1408 p.
S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach: textbook,” Pearson, 2019, 1104 p.
D. Practical, “Artificial Intelligence with Python,” Williams, 2019, 448 p.
M. Bernard, “Artificial Intelligence in Practice,” Wiley, 2019, 605 p.
Statistica software package, [Online]. Available: https://statsoft.ru, [Accessed: 04 Dec 2023].
M. F. H. Mohammad, “Comparison Markov Chain and Neural Network Models for forecasting Population growth data in Iraq,” University of Kirkuk Journal For Administrative and Economic Science, 13(4), pp. 1-14, 2023.
W. Rajesh, “Neural Network Based Mortality Prediction in Covid-19 Dataset,” University of Kirkuk International Conference on Artificial Intelligence of Things, 2024.