Research on National Sports Events Based on Hybrid Modeling Framework

Abstract

This study proposes a hybrid probabilistic framework integrating ARIMA-MCMC temporal modeling and Bayesian networks to address performance forecasting challenges in large-scale competitive systems. The framework combines ARIMA's capability to capture nonlinear temporal dependencies with MCMC's adaptive sampling for robust parameter optimization, while Bayesian networks quantify causal relationships among socioeconomic, demographic, and geopolitical variables. Validation on historical datasets (1948–2024) demonstrates high prediction accuracy, with errors controlled below 5% for established participants and discriminative power (AUC = 0.93) in identifying breakthrough potential for emerging entities. Key innovations include a dynamic parameter-tuning mechanism for handling non-stationary data and a modular architecture enabling transferability to domains such as supply chain risk assessment and infrastructure demand forecasting. The model's sensitivity to critical parameters (e.g., participant scale) is systematically analyzed, revealing nonlinear amplification effects mitigated through regularization. Limitations in static correlation assumptions are acknowledged, with proposed enhancements leveraging real-time data assimilation and adaptive learning.