Understanding Lag-Based Feature Learning in Export and Import Trade Forecasting Using Machine Learning

This paper examines lag-based feature learning in export and import trade forecasting using machine learning. Rather than focusing on forecasting accuracy, the study aims to understand how models learn from historical trade values. Monthly export and import data are converted into a supervised learning framework using lagged features, where past trade values serve as inputs and the next month's trade value is the output. Tree-based machine learning models are applied due to their ability to capture nonlinear patterns and provide feature importance measures. Feature importance is used as the main analytical tool to identify which lagged trade values contribute most to model learning. The results show that recent export values play a dominant role, while selected medium-term import lags also influence learning. In contrast, distant historical lags have limited impact. These findings indicate that trade forecasting models rely on selective temporal dependencies rather than uniformly using all past observations, highlighting the usefulness of feature-based learning analysis in trade forecasting.