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GDP prediction has traditionally relied on linear models that struggle with the complexities of economic variables. Recent advancements in artificial intelligence, particularly through Generative Adversarial Networks (GANs), offer a more sophisticated approach to forecasting. A study focused on The Gambia demonstrates how GANs can significantly enhance prediction accuracy compared to classical machine learning models.
The study reveals that GANs achieved a remarkable 99% prediction accuracy, outperforming traditional models like Random Forest and XGBoost. This advancement is crucial for small economies, where data limitations often hinder reliable forecasting. By integrating AI into economic forecasting, policymakers can make more informed decisions, ultimately promoting sustainable growth and stability.
• GANs significantly outperform traditional models in GDP prediction accuracy.
• AI-driven forecasting aids policymakers in making informed economic decisions.
GANs consist of two neural networks that generate and evaluate synthetic economic data for accurate forecasting.
Machine learning models like Random Forest and XGBoost are compared against GANs for GDP prediction accuracy.
Economic forecasting utilizes AI to predict GDP trends, aiding in policy formulation and resource allocation.
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