Search Results

The COVID-19 Pandemic Has Caused Unprecedented Disruptions To Global Economies, Leading To Significant Contractions In Gross Domestic Product (GDP) Growth Rates Across Countries. The Unpredictable Spread Of The Virus, Strict Lockdowns, Supply Chain Interruptions, And Changes In Consumer Behavior Have Made Traditional Economic Forecasting Models Insufficient For Capturing The Nonlinear And Dynamic Effects Of The Pandemic. To Address This Challenge, This Study Proposes The Use Of Adaptive Boosting (AdaBoost), A Robust Ensemble Learning Technique, For Forecasting The Impact Of COVID-19 On GDP Growth Rates. The Model Integrates Historical GDP Data, COVID-19 Case Trends, Government Policy Measures, And Key Macroeconomic Indicators To Improve Predictive Accuracy. By Iteratively Combining Weak Learners, AdaBoost Effectively Captures Complex Relationships Between Pandemic-related Variables And Economic Performance. Experimental Results Demonstrate That The Proposed Approach Provides More Reliable Forecasts Compared To Conventional Regression Models, Offering Valuable Insights For Policymakers, Researchers, And Economists. This Study Highlights The Potential Of Machine Learning–based Forecasting Systems In Managing Economic Uncertainty During Global Crises Like COVID-19.