Data Structure Trend Prediction Based on XGBoost and DFR Integration

Abstract

This study proposes a predictive modeling framework based on XGBoost and ensemble learning techniques to forecast national outcomes and structural trends in large-scale competition datasets. First, a multi-dimensional feature system incorporating static, dynamic, and interaction variables is constructed to characterize country-specific attributes. Based on this, an XGBoost-based nonlinear regression model is developed to estimate both gold and total medal counts, while integrating host country effects and event-scale adjustments. Next, to address prediction challenges for countries with sparse historical performance, a hierarchical clustering strategy is implemented to identify potential first-time medal winners and assess rank shifts. Finally, a hybrid Difference-in-Differences and Random Forest Regression (DFR) model is introduced to evaluate the quantitative effect of external interventions, with a focus on marginal contribution estimation and residual analysis. Through these methods, the framework achieves improved predictive accuracy, enhanced interpretability of event-level influence, and a refined understanding of competition structure dynamics.