Theoretical Exploration of Generative AI Applications in Economic Forecasting During Global Pandemics

Authors

  • Paril Ghori   

DOI:

https://doi.org/10.32628/CSEIT217217

Keywords:

Artificial Intelligence, Data Integration, Economic Forecasting, Generative Adversarial Networks, Generative AI, Global Pandemics.

Abstract

The COVID-19 pandemic has underscored the importance of advanced predictive models in understanding and mitigating the global economic disruptions caused by health crises. This review paper explores the application of Generative Artificial Intelligence (GenAI) techniques for macroeconomic forecasting during pandemics, focusing on its ability to predict economic slowdowns, job losses, and stock market volatility. Through an in-depth analysis of recent research, we discuss various approaches, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other machine learning algorithms, used to model the complex economic behaviors witnessed during pandemics. GenAI models have demonstrated significant potential in capturing the dynamics of pandemic-induced recessions, simulating economic disruptions such as unemployment spikes, shifts in GDP, and changes in global trade. Notably, these models can generate realistic data by simulating numerous pandemic scenarios, allowing policymakers to assess various economic interventions. The ability of GenAI to integrate diverse datasets — including healthcare, labor market data, and fiscal measures — enhances its forecasting accuracy, providing more granular insights into specific regions and sectors affected by global health crises. While GenAI has shown promise in improving the accuracy and adaptability of macroeconomic forecasts, challenges remain in terms of model interpretability, data integration, and the limitations of historical data, particularly in highly uncertain environments. Nevertheless, the findings suggest that these technologies can offer valuable tools for proactive financial measures, informing policy decisions related to fiscal stimulus, labor market interventions, and recovery plans in the wake of global pandemics. This paper concludes by outlining the future directions for the development of GenAI-based forecasting systems and their integration into economic policy planning.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-03-30

Issue

Volume 7, Issue 2, March-April-2021

Section

Research Articles

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How to Cite

[1]
Paril Ghori , " Theoretical Exploration of Generative AI Applications in Economic Forecasting During Global Pandemics" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 2, pp.677-691, March-April-2021. Available at doi : https://doi.org/10.32628/CSEIT217217