A MULTI-MODEL ENSEMBLE LEARNING FRAMEWORK FOR COMPLEX EVENT FORECASTING
Keywords:
Forecast, Ensemble learning, XGBoost, LSTM, ElasticNetAbstract
Forecasting outcomes in complex and multi-factor environments remains a significant challenge due to nonlinear interactions, temporal dependencies, and high-dimensional variables. This study presents a robust multi-model ensemble framework designed for large-scale performance prediction in dynamic competitive scenarios. The approach integrates XGBoost for nonlinear feature interactions, LSTM for temporal sequence modeling, and ElasticNet for interpretable linear correlations, combined through a stacking ensemble strategy to improve predictive accuracy and robustness. Furthermore, the framework incorporates uncertainty quantification using confidence intervals, enabling risk-aware decision-making. Experimental validation demonstrates that the proposed ensemble model outperforms individual models across multiple evaluation metrics and provides interpretable, actionable insights for strategic resource planning and performance assessment.References
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