GDP prediction of The Gambia using generative adversarial networks - Jallow, Haruna; Gibba, Alieu; Mwangi, Ronald Waweru; Imboga, Herbert (Frontiers in Artificial Intelligence, 2025)
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 R2, mean absolute error (MAE), mean absolute percentage error (MAPE), and root- mean-square error (RMSE) were collected to evaluate the system’s accuracy. Among the models tested—Random Forest Regression (RF), XGBoost (XGB), and Support Vector Regression (SVR)—the Generative Adversarial Networks (GAN) model demonstrated superior performance, achieving the highest accuracy, which is 99% prediction accuracies. The most dependable model for capturing intricate correlations between GDP and its affecting components, however, RF and XGBoost, also achieved an accuracy of 98% each. This makes GAN the most desirable model for GDP prediction for our study. Through data analysis, this project aims to provide actionable insights to support strategies that sustain economic boom. This approach enables the generation of accurate GDP forecasts, offering a valuable tool for policymakers and stakeholders.