PREDICTING OLYMPIC MEDALS: UNVEILING THE INFLUENCES
Keywords:
Olympic medals, ARIMA, GBT, Host effectAbstract
This study focuses on the prediction of Olympic medal data and the quantitative analysis of its influencing factors, aiming to address the complexity and nonlinearity in performance data. The research primarily employs the Autoregressive Integrated Moving Average (ARIMA) model to forecast time-series trends in medal counts, capturing the inherent periodicity of medal acquisition. Furthermore, by integrating linear regression with the Gradient Boosting Tree (GBT) model, the study quantifies correlations among event structures, medal attainment, and the host effect. The primary innovation lies in establishing a multi-level comprehensive model that overcomes the limitations of traditional methods in handling time-series dependence. The research findings provide quantitative support for predicting future Olympic trends, offer a research reference for quantifying the host country's advantages, and provide a new perspective for sports policy-making and related academic research.References
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