Neural network approximation in forecasting economic risks
Keywords: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.
Abalkin, L. I. (1994). Economic security of Russia: threats and their reflection. Questions of economy, 12, 4-13.
Aleksandrova, O., Batchenko, L., Dielini, M., & Lavryk, U. (2018). Specifics of managing competitiveness of present-day university on principles of social responsibility. Scientific Bulletin of the National Mining University , (4), 157-165.
Anastassiou, G. A. (2011). Multivariate sigmoidal neural network approximation. Neural Networks, 24(4), 378-386. https://doi.org/10.1016/j.neunet.2011.01.003
Anastassiou, G. A. (2012). Fractional neural network approximation. Computers & Mathematics with Applications, 64(6), 1655-1676. https://doi.org/10.1016/j.camwa.2012.01.019
Arkhireiska, N.V. (2012). The main directions of ensuring the financial security of Ukraine. Bulletin of the Academy of Customs Service of Ukraine, 1, 83-87.
Arnawa, I.K., Sapanca, P.L.Y., Martini, L.K.B., Udayana, I.G.B., Suryasa, W. (2019). Food security program towards community food consumption. Journal of Advanced Research in Dynamical and Control Systems, 11(2), 1198-1210.
Chaika, P. (2020). Neural networks: the explanation with simple and clear language.
Chinyere, C. N. (2021). Fiscal deficit and Nigeria economic growth (1990-2020). International Research Journal of Management, IT and Social Sciences, 8(5), 411-433. https://doi.org/10.21744/irjmis.v8n5.1915
Codeguida. (2017). Neural networks – a way to profound education.
De Grenade, R., House-Peters, L., Scott, C. A., Thapa, B., Mills-Novoa, M., Gerlak, A., & Verbist, K. (2016). The nexus: reconsidering environmental security and adaptive capacity. Current opinion in environmental sustainability, 21, 15-21. https://doi.org/10.1016/j.cosust.2016.10.009
Evergreen. (2021). The development and stuying the neural network.
Filippova, O. V., Grigoriev, A. V., Murzagalina, G. M., Sorgutov, I. V., Latifzoda, D. N., & Kalimullin, D. D. (2021). Trends in economic development and education of future economists. Linguistics and Culture Review, 5(1), 397-405. https://doi.org/10.21744/lingcure.v5n1.1842
Grebennik, I., Khriapkin, O., Ovezgeldyyev, A., Pisklakova, V., & Urniaieva, I. (2017, October). The concept of a regional information-analytical system for emergency situations. In International Conference on Information Technology in Disaster Risk Reduction (pp. 55-66). Springer, Cham.
Grigoreva, E., & Garifova, L. (2015). The economic security of the state: the institutional aspect. Procedia Economics and Finance, 24, 266-273. https://doi.org/10.1016/S2212-5671(15)00658-9
Haleshchuk, S. (2016). Artificial neural networks in foreign exchange market forecasting. Visnyk of Kyiv National University of Trade and Economics, 3, 101-114.
Hertel, T. W., & Baldos, U. L. C. (2016). Attaining food and environmental security in an era of globalization. Global environmental change, 41, 195-205. https://doi.org/10.1016/j.gloenvcha.2016.10.006
Horbulin, V.P., & Vlasiuk, O.S. (2015). Analytical Report for the Annual Address of President of Ukraine to the Verkhovna Rada of Ukraine “About the internal and external situation of Ukraine in 2015”. Kyiv: National Institute of Strategic Research.
Hornik, K. (1993). Some new results on neural network approximation. Neural networks, 6(8), 1069-1072. https://doi.org/10.1016/S0893-6080(09)80018-X
Hubarieva, I.O., & Pinchuk, A.O. (2015). Assessment of the level of economic security formation in Ukraine and EU countries. Business Inform, 15, 293-297.
Imamov, M., & Semenikhina, N. (2021). The impact of the digital revolution on the global economy. Linguistics and Culture Review, 5(S4), 968-987. https://doi.org/10.21744/lingcure.v5nS4.1775
Jarrow, R., & Rudd, A. (1982). Approximate option valuation for arbitrary stochastic processes. Journal of financial Economics, 10(3), 347-369. https://doi.org/10.1016/0304-405X(82)90007-1
Karyntseva, A.I. (1997). Economic bases of planning of processes of ecologically sustainable development of the territory. Sumy: Sumy State University.
Khomutenko, A., Mishchenko, A., Ripenko, A., Frum, O., Liulchak, Z., & Hrozovskyi, R. (2019). Tools of the neuro-fuzzy model of information risk management in national security. International Journal of Engineering and Advanced Technology, 8(6), 4526-4530.
Khvesyk, M. A., & Stepanenko, A. V. (2014). Ecologic crisis in Ukraine: social and economic consequences and the ways of its overcoming. Economy of Ukraine, 1, 74-86.
Kiumarsi, B., Lewis, F. L., & Levine, D. S. (2015). Optimal control of nonlinear discrete time-varying systems using a new neural network approximation structure. Neurocomputing, 156, 157-165. https://doi.org/10.1016/j.neucom.2014.12.067
Lagodiienko, V., Karyy, O., Ohiienko, M., Kalaman, O., Lorvi, I., & Herasimchuk, T. (2019). Choosing effective internet marketing tools in strategic management. International Journal of Recent Technology and Engineering, 8(3), 5220-5225.
Len, W., & Hoang, R. (2019). Problem towards local language translation in artificial neural networks. Applied Translation, 13(2), 8–15. Retrieved from https://appliedtranslation.nyc/index.php/journal/article/view/522
Penn, H. J., Loring, P. A., & Schnabel, W. E. (2017). Diagnosing water security in the rural North with an environmental security framework. Journal of environmental management, 199, 91-98. https://doi.org/10.1016/j.jenvman.2017.04.088
Terianyk, O. A. (2015). Assessment of sustainable ecological development of the region. Effective Economy, 5.
Tymoshenko, O. V. (2016). Using Integral Assessment Of Economic Security And Its Main Functional Components. Aktual'ni Problemy Ekonomiky= Actual Problems in Economics, (183), 95.
Tymoshenko, O.V., & Kotsiubivska, K.I. (2016). Approaches to determining the weights of integrated indices of economic security of the national economy. Black Sea Economic Studies, 8, 230-235.
Yang, B., Li, L. X., Ji, H., & Xu, J. (2001). An early warning system for loan risk assessment using artificial neural networks. Knowledge-Based Systems, 14(5-6), 303-306. https://doi.org/10.1016/S0950-7051(01)00110-1
Yusroni, N., & Chadhiq, U. (2021). Understanding the impact of zakat and waqf as economic development of the community in rural areas. International Research Journal of Management, IT and Social Sciences, 8(6), 639-647. https://doi.org/10.21744/irjmis.v8n6.1966
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