Medal Prediction Analysis Based on ARIMA Time Series Model with Random Forests

The purpose of this paper is to predict and analyze the number of medals of Olympic countries based on ARIMA time series model and random forest. Firstly, the ARIMA time series model and random forest model are used to pre-process the Olympic historical data to ensure the data quality and consistency. Then, the number of medals is predicted and evaluated from the dimensions of time series characteristics and nonlinear relationships. Meanwhile, the comprehensive evaluation method is used to deeply analyze the influence of coaches on the number of medals. The strength of the study lies in the combination of multiple models, which gives full play to the advantages of ARIMA time series model in dealing with time series features and the ability of Random Forest model in mining nonlinear relationships, and comprehensively improves the accuracy of prediction.