The role of artificial intelligence and machine learning in forecasting economic trends
Introduction: The globalisation of the economy, dynamic changes in financial markets, and the advent of big data have spurred the development and implementation of artificial intelligence (AI) and machine learning (ML) tools for forecasting economic trends. The purpose of this study is to evaluate the impact of AI and ML on the accuracy and effectiveness of economic trend forecasting. The authors analyse examples of AI and ML applications in various economic sectors during the period 2019–2023, including regional aspects. Methods: To achieve the objectives of this study, we conducted a comprehensive qualitative and quantitative analysis of the role of artificial intelligence (AI) and machine learning (ML) in predicting economic trends. Results: The findings indicate that the use of AI and ML improves the efficiency of economic trend forecasting and allows for quicker adaptation to market changes, thereby reducing risks and uncertainty. Conclusions: Thus, the integration of artificial intelligence and machine learning in economic analysis not only increases the effectiveness of forecasting but also lays the foundations for the sustainable development of economies in a globalised world.
Authors
- Світлана Марущак (ORCID: https://orcid.org/0000-0002-0760-4427)
- Petar Halachev (ORCID: https://orcid.org/0009-0008-2159-406X)
- Iryna Fadyeyeva
- Nursultan Zharkenov (ORCID: https://orcid.org/0000-0002-8717-3259)
- Sergii Pakhomov
Institutions
- Admiral Makarov National University of Shipbuilding (UA)
- National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (UA)
- Ivano-Frankivsk National Technical University of Oil and Gas (UA)
- Abylkas Saginov Karaganda Technical University (KZ)
- University of Chemical Technology and Metallurgy (BG)
Publication Details
- Journal
- Data & Metadata
- Published
- 2024-11-12
- DOI
- https://doi.org/10.56294/dm2024.247
- Citations
- 3
- Primary Topic
- Stock Market Forecasting Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.47