Neural network approximation in forecasting economic risks


  • Kateryna I. Kotsiubivska Kyiv National University of Culture and Arts, Kyiv, Ukraine
  • Olena V. Tymoshenko Kyiv National University of Culture and Arts, Kyiv, Ukraine
  • Olena A. Chaikovska Kyiv National University of Culture and Arts, Kyiv, Ukraine
  • Maryna S. Tolmach Kyiv National University of Culture and Arts, Kyiv, Ukraine
  • Svitlana S. Khrushch Kyiv National University of Culture and Arts, Kyiv, Ukraine


approximation, economic security, environmental security, neural networks risk, assessment criteria


The article considers methodological approaches to assessing the level of development of economic systems in the context of increasing the accuracy of forecasts in unpredictable socio-economic conditions in particular taking into account the impact of unforeseen environmental risks and disasters. The author's used methods to approximate economic criteria with the help of neural networks. Analyzing the criteria of economic development of different countries, as well as taking into account the factors of the macroeconomic environment, a neural network approximation model of risk forecasting in the economic development of the country has been developed. To date, a large number of mathematical forecasting methods are known, and experts in the world economy use appropriate risk assessment criteria, but the neural network is used when the exact type of connections between inputs and outputs is unknown, which allows us to create a more accurate and flexible forecast model. The modeling takes into account the main weights that determine the degree and the priority of the impact on each component of the economic system and characterizes the complex macroeconomic relationships to determine the aggregate indices.


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How to Cite

Kotsiubivska, K. I., Tymoshenko, O. V., Chaikovska, O. A., Tolmach, M. S., & Khrushch, S. S. (2021). Neural network approximation in forecasting economic risks. Linguistics and Culture Review, 5(S4), 1830-1841.



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