Forecasting Indicators of Economic Development of Ukraine Using an Artificial Neural Network

Stanislav Munka, Yevhenii Kyryliuk

Abstract

The article presents the results of forecasting indicators that characterise the economic development of Ukraine with the help of an artificial neural network developed by the author. For this purpose, a fully connected artificial neural network of direct signal propagation with a sigmoidal activation function was used. An inverse error propagation algorithm was used to train the artificial neural network. Used to assess the quality of the neural network Mean Squared Error and Mean Absolute Percentage Error. According to the forecasting results for the period 2022–2023, we have: a slight decline in Ukraine's GDP (US dollars), the growing role of the private sector in the structure of fixed assets of the country, and investment growth and growth in export earnings. At the same time, it is possible to reduce the amount of investment by the state and reduce the average salary (US dollars). Within the forecast period, the number of employed people in Ukraine will remain stable



Keywords


prognostication; artificial neural network; sigmoidal activation function; inverse error propagation algorithm; Mean Squared Error; Mean Absolute Percentage Error

References


Terekhov, V., & Zhukov, R. (2017). Technique for preparing data for processing by impulse neural networks. Neurocomputers: development, application, 2, 31-36.

Charkin E. Y. (2017). Strategic development of information technology and communications. Automation, communications, informatics, 4, 2-5.

Kulnevych A. D. (2017). Introduction to neural networks. Young scientist, 8, 31-36.

Parret, A. (2020). Neural networks in economics (Doctoral dissertation). Retrieved from https://www.proquest.com/openview/562337133dfbb84e44cf268cd591a7d7/1?pq-origsite=gscholar&cbl=18750&diss=y/ .

Shotylo, D., Krainova, V., & Skurydin, A. (2018). Trends in the development of artificial neural networks in the digital economy. Information technologies in enterprise management, 15(4), 65–69.

State Statistics Service of Ukraine. (2022). About. Retrieved from http://www.ukrstat.gov.ua

The Ministry of Economic of Ukraine. (2022). About. Retrieved from https://www.me.gov.ua/?lang=en-GB

Kallan, R. (2001). Basic concepts of neural networks. New York: Williams Publishing House.

1. Terekhov, V., & Zhukov, R. (2017). Technique for preparing data for processing by impulse neural networks. Neurocomputers: development, application, 2, 31-36.

2. Charkin E. Y. (2017). Strategic development of information technology and communications. Automation, communications, informatics, 4, 2-5.

3. Kulnevych A. D. (2017). Introduction to neural networks. Young scientist, 8, 31-36.

4. Parret, A. (2020). Neural networks in economics (Doctoral dissertation). Retrieved from https://www.proquest.com/openview/562337133dfbb84e44cf268cd591a7d7/1?pq-origsite=gscholar&cbl=18750&diss=y/ .

5. Shotylo, D., Krainova, V., & Skurydin, A. (2018). Trends in the development of artificial neural networks in the digital economy. Information technologies in enterprise management, 15(4), 65–69.

6. State Statistics Service of Ukraine. (2022). About. Retrieved from http://www.ukrstat.gov.ua

7. The Ministry of Economic of Ukraine. (2022). About. Retrieved from https://www.me.gov.ua/?lang=en-GB

Kallan, R. (2001). Basic concepts of neural networks. New York: Williams Publishing House.


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Copyright (c) 2022 Stanislav Munka, Yevhenii Kyryliuk

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