Using Neuronets to Forecast Economic Processes in Conditions of Uncertainty
https://doi.org/10.21686/2413-2829-2025-4-77-86
Abstract
Lately we can observe the trend of fast development of neuronet technologies. Neuronets are researched by different sciences: economic, mathematic, informational, statistic, etc. Advantages of using neuronets in various types of activities are as follows: adaptability, possibility to process big amounts of variables, high degree of reliability and opportunity to retune the model when parameters are changed. Models of neuronet forecasting are widely used in finance and economic field, as it often uses analytical indicators. The article shows benefits of neuronet models in comparison with traditional ways of processing information to forecast economic figures. Neuronets give an opportunity to trace dynamics of various economic indicators: inflation rate, consumer prices, human capital, etc. Apart from that neuronets can be combined with other ways of information processing, for example, economic ones, which can provide extra opportunities of their use. The authors show prospects of neuronet development in analytical systems.
About the Authors
N. V. Barinova
Plekhanov Russian University of Economics
Russian Federation
Russian Federation
Natalya V. Barinova - PhD, Leading Specialist E-Learning Development Center of the PRUE.
36 Stremyanny Lane, Moscow, 109992
V. R. Barinov
Moscow Polytechnic University
Russian Federation
Russian Federation
Vladimir R. Barinov - Post-Graduate Student of the Department for Infocognitive Technologies of the Moscow Poly.
38 B. Semenovskaya Str., Moscow, 105094
References
1. Apatova N. V., Popov V. B. Prognozirovanie bankrotstva predpriyatiy s ispolzovaniem iskusstvennogo intellekta [Forecasting Bankruptcy of Enterprises by Using AI]. Nauchniy vestnik: finansy, banki, investitsii [Academic Bulletin: Finance, Banks, Investment], 2020, No. 2 (51), pp. 113–120. (In Russ.).
2. Balatskiy E. V., Ekimova N. A., Yurevich M. A. Kratkosrochnoe prognozirovanie inflyatsii na osnove markernykh modeley [Short-Term Forecasting of Inflation Rate Based on Marker Models]. Problemy prognozirovaniya [Forecast Problems], 2019, No. 5 (176), pp. 28–40. (In Russ.).
3. Balatskiy E. V., Yurevich M. A. Ispolzovanie neyronnykh setey dlya prognozirovaniya inflyatsii: novye vozmozhnosti [The Use of Neuronets to Forecast Inflation Rate: New Opportunities]. Vestnik UrFU. Seriya: Ekonomika i upravlenie [Bulletin of the Ural Federal University. Series: Economics and Management], 2018, Vol. 17, No. 5, pp. 823–838. (In Russ.).
4. Balatskiy E. V., Yurevich M. A. Prognozirovanie inflyatsii: praktika ispolzovaniya sinteticheskikh protsedur [Inflation Rate Forecasting: Practice of Using Synthetic Procedures]. Mir novoy ekonomiki [The World of New Economics], 2018, Vol. 12, No. 4, pp. 20–31. (In Russ.).
5. Barinova N. V., Barinov V. R. Primenenie sistem iskusstvennogo intellekta dlya dostizheniya tseley ustoychivogo razvitiya [Using AI Systems to Reach Goals of Sustainable Development]. Vestnik Rossiyskogo ekonomicheskogo universiteta imeni G. V. Plekhanova [Vestnik of the Plekhanov Russian University of Economics], 2023, Vol. 20, No. 6 (132), pp. 26–36. (In Russ.).
6. Barinova N. V., Barinov V. R. Tsifrovaya ekonomika, iskusstvenniy intellekt, industriya 5.0: vyzovy sovremennosti [Digital Economy, AI, Industry 5.0: Current Challenges]. Vestnik Rossiyskogo ekonomicheskogo universiteta imeni G. V. Plekhanova [Vestnik of the Plekhanov Russian University of Economics], 2022, Vol. 19, No. 5 (125), pp. 23–34. (In Russ.).
7. Vavilova D. D., Ketova K. V. Neyrosetevaya model prognozirovaniya chelovecheskogo kapitala [Neuronet Model of Forecasting Human Capital]. Intellektualnye sistemy v proizvodstve [Intellectual Systems in Production], 2020, Vol. 18, No. 1, pp. 26–35. (In Russ.).
8. Vedyakhin A. Silniy iskusstvenniy intellekt: na podstupakh k razumu [Strong AI: on the Way to Intellect]. Iskusstvenniy intellekt: na podstupakh k sverkhrazumu [AI: on the Way to SuperIntellect]. Moscow, Intellectual Literature, 2021. (In Russ.).
9. Ketova K. V., Vavilova D. D. Otsenka tendentsiy izmeneniya chelovecheskogo kapitala sotsialno-ekonomicheskoy sistemy na osnove primeneniya algoritma neyrosetevogo prognozirovaniya [Assessing Trends of Changing Human Capital of Social and Economic System on the Base of Using Algorithm of Neuronet Forecast]. Ekonomicheskie i sotsialnye peremeny: fakty, tendentsii, prognoz [Economic and Social Changes: Facts, Trends, Forecast], 2020, Vol. 13, No. 6, pp. 117–133. (In Russ.).
10. Krayushkin M. G. Sovershenstvovanie prognozirovaniya sotsialno-ekonomicheskogo razvitiya regiona [Upgrading the Forecast of Social and Economic Development of Region]. Ekonomika i upravlenie: problemy, resheniya [Economics and Management: Challenges, Solutions], 2024, Vol. 2, No. 4 (145), pp. 124–132. (In Russ.).
11. Selyutin M. I. Postroenie imitatsionnoy modeli prognozirovaniya neplatezhesposobnosti predpriyatiya s primeneniem tekhnologii neyronnykh setey [Building Imitation Models of Forecasting Enterprise Insolvency by Using Technology of Neuronet Networks]. Trudy Dalnevostochnogo politekhnicheskogo instituta im. V. V. Kuybysheva [Works of the Far-East V. V. Kuibyshev Polytechnics], 2000, No. 127, pp. 132–138. (In Russ.).
12. Aiken M. Using a Neural Network to Forecast Inflation. Industrial Management & Data Systems, 1999, Vol. 99, Issue 7, pp. 296–301.
13. Alon I., Qi M., Sadowski R. J. Forecasting Aggregate Retail Sales: a Comparison of Artificial Neural Net-Works and Traditional Methods. Journal of Retailing and Consumer Services, 2001, Vol. 8, Issue 3, pp. 147–156.
14. Altman E., Hotchkiss E. Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt. 3rd edition. John Wiley and Sons, Ltd., 2006.
15. Aminian F., Suarez E. D., Aminian M., Walz D. T. Forecasting Economic Data with Neural Networks. Computational Economics, 2006, Vol. 28, Issue 1, pp. 71–88.
16. Beaver W. H. Financial Ratios as Predictors of Failure, Empirical Research in Accounting Selected Studies. Supplement to Journal of Accounting Research, 1966, No. 4, pp. 71–111.
17. Faust J., Wright J. H. Comparing Greenbook and Reduced form Forecasts Using a Large Real-Time Dataset. Journal of Business & Economic Statistics, 2009, Vol. 27 (4), pp. 468–479.
18. Lockard R. P., Zmazneva O. A., Volnov I. N. Artificial Intelligence: Are Humans Protected from the Systems they Created? Vestnik MGPU. Seriya «Filosofskie nauki», 2021, No. 3 (39), pp. 47–55.
19. Swanson N. R., White H. A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks. Review of Economics and Statistics, 1997, Vol. 79, Issue 4, pp. 540–550.
Review
For citations:
Barinova N.V., Barinov V.R. Using Neuronets to Forecast Economic Processes in Conditions of Uncertainty. Vestnik of the Plekhanov Russian University of Economics. 2025;(4):77-86. (In Russ.) https://doi.org/10.21686/2413-2829-2025-4-77-86
Views:
14
Email this article 