Improving Investment Forecasting: A Comparative Analysis of Machine Learning Models as Key GDP Indicators

Abstract

Investment is a crucial driver of economic growth and a fundamental component of Gross Domestic Product (GDP). Accurate investment forecasting is essential for informed policymaking and economic planning. However, traditional econometric models often struggle to capture economic data’s intricate and non-linear dynamics, indicating a need for more robust and adaptable approaches. This study addresses this need by employing machine learning techniques to enhance investment forecasting accuracy, particularly the Gated Recurrent Unit (GRU) model. Historical investment data from fifteen leading GDP countries (1990–2020) was analyzed using seven models: GRU, ARIMA, ETS, SVR, XGBoost, CNN, and LSTM. Key performance metrics—Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE)—demonstrated that the GRU model achieved superior accuracy. The GRU model consistently outperformed other models across all performance metrics. It achieved the lowest MAE, RMSE, and MAPE values, highlighting its effectiveness in capturing complex temporal dependencies. Compared to traditional econometric models, GRU delivered significantly more accurate forecasts, emphasizing its potential for improving investment predictions. The results emphasize the potential of advanced machine learning models in capturing complex temporal dependencies, leading to more reliable investment predictions. This study addresses a significant gap in economic forecasting by highlighting the advantages of GRU and its implications for policymaking and future research.