用时空图网络和长短期记忆预测奥运奖牌，区分偶然无奖与根本无竞争力。
STGCN-LSTM for Olympic Medal Prediction: Dynamic Power Modeling and Causal Policy Optimization
- 融合地理互动与历史趋势的混合模型STGCN-LSTM
- 零膨胀复合泊松模型有效分离偶然无奖与结构性无奖
- 可模拟政策冲击，适合体育决策者参考
本文提出一种新型混合模型STGCN-LSTM，通过整合国家间的时空关系与长期表现趋势，预测奥运会奖牌分布。空间-时间图卷积网络（STGCN）捕捉地理因素及教练交流、社会经济联系等互动特征，长短期记忆（LSTM）模块建模奖牌数、经济数据与人口统计的历史演变。为应对零膨胀输出问题（即持续无奖国与从未获奖国之间的差异），引入零膨胀复合泊松（ZICP）框架，将随机零与结构零分离，更清晰揭示潜在突破性表现。通过历史回溯、政策冲击模拟与因果推断验证，结果表明教练流动性、项目专精化与战略投资显著影响奖牌预测，为不同奥运背景下的体育政策优化与资源分配提供数据驱动基础。
原文摘要 · Abstract (English)
This paper proposes a novel hybrid model, STGCN-LSTM, to forecast Olympic medal distributions by integrating the spatio-temporal relationships among countries and the long-term dependencies of national performance. The Spatial-Temporal Graph Convolution Network (STGCN) captures geographic and interactive factors-such as coaching exchange and socio-economic links-while the Long Short-Term Memory (LSTM) module models historical trends in medal counts, economic data, and demographics. To address zero-inflated outputs (i.e., the disparity between countries that consistently yield wins and those never having won medals), a Zero-Inflated Compound Poisson (ZICP) framework is incorporated to separate random zeros from structural zeros, providing a clearer view of potential breakthrough performances. Validation includes historical backtracking, policy shock simulations, and causal inference checks, confirming the robustness of the proposed method. Results shed light on the influence of coaching mobility, event specialization, and strategic investment on medal forecasts, offering a data-driven foundation for optimizing sports policies and resource allocation in diverse Olympic contexts.
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