ArticleFrontiers in artificial intelligence2025
GDP prediction of The Gambia using generative adversarial networks.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Predicting Gross Domestic Product (GDP) is one of the most crucial tasks in analyzing a nation's economy and growth. The primary goal of this study is to forecast GDP using factors such as government spending, inflation, official development aid, remittance inflows, and Foreign Direct Investment (FDI). Additionally, the paper aims to provide an alternative perspective to Generative Adversarial Networks method and demonstrate how such deep learning technique can enhance the accuracy of GDP predictions with small data and economy like The Gambia. We proposed the implementation of Generative Adversarial Networks to predict GDP using various economic factors over the period from 1970 to 2022. Performance metrics, including the coefficient of determination R
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