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dc.contributor.authorСемеріков, Сергій Олексійович-
dc.contributor.authorKucherova, Hanna-
dc.contributor.authorLos, Vita-
dc.contributor.authorOcheretin, Dmytro-
dc.date.accessioned2021-06-21T15:59:48Z-
dc.date.available2021-06-21T15:59:48Z-
dc.date.issued2021-04-10-
dc.identifier.citationSemerikov S. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (COVID-19) [Electronic resource] / Serhiy Semerikov, Hanna Kucherova, Vita Los, Dmytro Ocheretin // Proceedings of the 7th International Conference "Information Technology and Interactions" (IT&I-2020). Workshops Proceedings. Kyiv, Ukraine, December 02-03, 2020 / Edited by : Vitaliy Snytyuk, Anatoly Anisimov, Iurii Krak, Mykola Nikitchenko, Oleksandr Marchenko, Frederic Mallet, Vitaliy Tsyganok, Aldrich Chris, Andreas Pester, Hiroshi Tanaka, Karsten Henke, Oleg Chertov, Sándor Bozóki, Vladimir Vovk // CEUR Workshop Proceedings. – 2021. – Vol. 2845. – P. 22-32. – Access mode : http://ceur-ws.org/Vol-2845/Paper_3.pdfuk
dc.identifier.issn1613-0073-
dc.identifier.urihttp://ceur-ws.org/Vol-2845/Paper_3.pdf-
dc.identifier.urihttp://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4364-
dc.identifier.urihttps://doi.org/10.31812//123456789/4364-
dc.description[1] S. Arslankaya, V. Öz. Sakarya. "Time Series Analysis on Sales Quantity in an Automotive Company and Estimation by Artificial Neural Networks." University Journal of Science, Vol. 22, (2018): 1482-1492. DOI: 10.16984/saufenbilder.456518. [2] D. Ocheretin, V. Los, H. Kucherova, O. Bilska. "An alternative approach to modeling the country's business climate in conditions of limited information." E3SWC 166 (2020): 13024. URL: https://www.e3s-conferences.org/articles/e3sconf/abs/2020/26/e3sconf_icsf2020_13024/e3sconf_ icsf2020_13024.html. [3] European Business Association. URL: https://eba.com.ua/research/doslidzhennya-ta-analityka. [4] L.A. El'shin. "Mechanisms for the identification of business cycles of regional economic systems based on cross-correlation analysis." Regional Economics: Theory and Practice Vol. 15(8), (2017): 1540-1551. DOI: 10.24891/re.15.8.1540. [5] V. Los, D. Ocheretin, H. Kucherova, O. Bilska. "Neural network technology forecasting the country's business climate." Proceedings of the 6th International Conference on Strategies, Models and Technologies of Economic Systems Management (SMTESM 2019) 95, Atlantis Press (2019): 320-324. DOI: 10.2991/smtesm-19.2019.62. [6] M. R. Safiullin, L.A. El'shin, A.I. Shakirova. "Evaluation of business and economic activity as a short-term forecasting tool." Herald of the Russian Academy of Sciences, Vol. 82(4), (2012): 623- 627. DOI: 10.1134/S1019331612040053 [7] S. Feuerriegela, J. Gordon. "News-based forecasts of macroeconomic indicators: а semantic path model for interpretable predictions." European Journal of Operational Research, Vol. 272(1), (2019): 162-175. DOI: 10.1016/j.ejor.2018.05.068. [8] H. F. Mendonca, A. F. G. Almeida. "Importance of credibility for business confidence: evidence from an emerging economy." Empirical Economics (2018). DOI: 10.1007/s00181-018- 1533-5. [9] H. Sakaji, R. Kuramoto, H. Matsushima, K. Izumi, T. Shimada, K. Sunakawa. "Financial Text Data Analytics Framework for Business Confidence Indices and Inter-Industry Relations." Proceedings of the First Workshop on Financial Technology and Natural Language Processing (FinNLP@IJCAI 2019), Macao, China (2019): 40-46. [10] V. Los, D. Ocheretin. "Construction of business confidence index based on a system of economic indicators." SHS Web of Conference. The 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019), vol.65, SHS Web of Conferences (2019): 1-6. DOI: 10.1051/shsconf/20196506003. [11] V. Los, D. Ocheretin. "Forecasting of the business confidence index of Ukraine on the basis of neural networks technologies." Bulletin Zaporizhzhia national university. Economic sciences. Vol. 3(43), (2019): 55-60. URL: http://journalsofznu.zp.ua/index.php/economics/article/view/89. [12] National bank of Ukraine. Business expectations of enterprises, URL: https://bank.gov.ua/control/uk/publish/ category? cat_id=58374. [13] Tradingeconomics.com. Ukraine Exports. URL: https://tradingeconomics.com/ukraine/ exports. [14] Tradingeconomics.com. Ukraine GDP Annual Groth Rate. URL: https://tradingeconomics.com/ukraine/ gdp-growth-annual. [15] Tradingeconomics.com. Ukraine GDP Consumer Spending. URL: https://tradingeconomics.com/ukraine/consumer-spending. [16] Tradingeconomics.com. Ukraine Imports. URL: https://tradingeconomics.com/ukraine/ imports. [17] Tradingeconomics.com. Ukraine Industrial Production. URL: https://tradingeconomics.com/ ukraine/industrial-production. [18] Tradingeconomics.com. Ukraine Money Supply M1. URL: https://tradingeconomics.com/ ukraine/money-supply-m1. [19] Tradingeconomics.com. Ukraine Money Supply M2. URL: https://tradingeconomics.com/ ukraine/money-supply-m2. [20] Tradingeconomics.com. Ukraine Money Supply M3. URL: https://tradingeconomics.com/ ukraine/money-supply-m3. [21] Tradingeconomics.com. Ukraine Retail Sales YoY. URL: https://tradingeconomics.com/ ukraine/retail-sales-yoy. [22] Tradingeconomics.com. Ukraine Steel Production. URL: https://tradingeconomics.com/ ukraine/steel-production. [23] Tradingeconomics.com. Ukraine Unemployment Rate. URL: https://tradingeconomics.com/ukraine/unemployment-rate. [24] Global Risks report 2020. URL: https://www.weforum.org/reports/the-global-risks-report2020. [25] I.S. Mintii, V. N. Soloviev. "Augmented Reality: Ukrainian Present Business and Future Education." Proceedings of the 1st International Workshop on Augmented Reality in Education, Vol. 2257, CEUR-WS.org (2018): 227-231. URL: http://lib.iitta.gov.ua/712804/1/paper22.pdf.-
dc.description.abstractThe prospects for doing business in countries are also determined by the business confidence index. The purpose of the article is to model trends in indicators that determine the state of the business climate of countries, in particular, the period of influence of the consequences of COVID-19 is of scientific interest. The approach is based on the preliminary results of substantiating a set of indicators and applying the taxonomy method to substantiate an alternative indicator of the business climate, the advantage of which is its advanced nature. The most significant factors influencing the business climate index were identified, in particular, the annual GDP growth rate and the volume of retail sales. The similarity of the trends in the calculated and actual business climate index was obtained, the forecast values were calculated with an accuracy of 89.38%. And also, the obtained modeling results were developed by means of building and using neural networks with learning capabilities, which makes it possible to improve the quality and accuracy of the business climate index forecast up to 96.22%. It has been established that the consequences of the impact of COVID-19 are forecasting a decrease in the level of the country's business climate index in the 3rd quarter of 2020. The proposed approach to modeling the country's business climate is unified, easily applied to the macroeconomic data of various countries, demonstrates a high level of accuracy and quality of forecasting. The prospects for further research are modeling the business climate of the countries of the world in order to compare trends and levels, as well as their changes under the influence of quarantine restrictions.uk
dc.language.isoenuk
dc.publisherCEUR Workshop Proceedingsuk
dc.subjectbusiness climateuk
dc.subjectindexuk
dc.subjectcorrelation analysisuk
dc.subjectindicatorsuk
dc.subjecttaxonomic modeluk
dc.subjectneural network modeluk
dc.subjectCOVID-19uk
dc.titleNeural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (COVID-19)uk
dc.typeArticleuk
Розташовується у зібраннях:Кафедра інформатики та прикладної математики

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