Forecast of Stability of the Economy of the Russian Federation with the AI-System “Decision Tree” in a Cognitive Model
Increased use of modern mathematical algorithms based on artificial intelligence determined the relevance of this study, which is important for predicting the sustainable development of the country's economy in general and its banking sector in particular. To achieve the purpose of the rese...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2023-12-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/6848 |
| Summary: | Increased use of modern mathematical
algorithms based on artificial intelligence determined the relevance of this
study, which is important for predicting the sustainable development of the
country's economy in general and its banking sector in particular. To achieve
the purpose of the research, the presented work used methods such as
monographic, analytical, statistical, cognitive model, and artificial
intelligence system "Random Forest". The aim of the study is to prove
or disprove the hypothesis that, using a cognitive model, using the Random
Forest ML model, it is possible to obtain an accurate forecast of the value of
the "sustainability coefficient", reflecting the stability of the
domestic economy. The scientific novelty of the study is due to the fact that
the author's approach is proposed for indicating the crisis state of the
economy through the calculation and neural network forecasting by the machine
learning model "Random Forest" of the "Stability
Coefficient" of the economy, which is calculated as the quotient of dividing
the profit index of the banking system to the GDP growth index. The possibility
of practical application in the banking sector determines the practical
significance of the conducted scientific research since the approach proposed
by the authors regarding the formation of a forecast of the “sustainability
coefficient” can be successfully used to support managerial decision-making at
the strategic level in the banking system. A hypothesis was put forward and
proven that based on the use of a digital cognitive model and the Random Forest
ML system, a forecast of economic stability can be successfully generated. |
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| ISSN: | 2086-9614 2087-2100 |