AI-Driven Predictive Analytics for Forecasting Global Trade Flows

Abstract

The pandemic, geopolitical and sustainability pressures have taken the global trade to a stage of a higher volatility and it needs a more complex way of predictions. The provided work is a compilation of and empirical validation of an artificial intelligence-based predictive analytics model of forecasting international trade movement through the use of machine learning (ML), deep learning (DL) and explainable artificial intelligence (XAI) applications. The study compares the AI models with the more traditional econometric models such as the Gravity Model and the Vector Autoregression (VAR) based on the assumption of the data in UN Comtrade, World Bank Indicators, OECD trade statistics and personal shipping data 2000-2024. The results show that AI models, namely, LSTM, Bi-LSTM and hybrid ensembles would tend to achieve better systematically compared to the traditional models in terms of error metrics, which are RMSE, MAE and MAPE. The model also reveals that it is resilient to the shock that the COVID-19 pandemic and the clash between Russia and Ukraine inflict, and the further integration of ESG and the carbon-intensity indicators add to the long-term forecasts of sustainable trade flows. Transparency is given by the SHAP-based interpretability that determines GDP, exchange rates and commodity prices as the most effective forces. These findings confirm that the AI-driven predictive analytics is not a vacuum as far as its technical accuracy is concerned but offers actionable insights regarding the policy/strategy, and thus, it is an essential tool in regulating trade flows in a highly unpredictable and sustainability-conscious global economy.