Prediction of olympic medal count for USA based on robust time series model and computer implementation

Zhijun He; Zhijian Wang

doi:10.1117/12.3033012

5 July 2024 Prediction of olympic medal count for USA based on robust time series model and computer implementation

Zhijun He, Zhijian Wang

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131845E (2024) https://doi.org/10.1117/12.3033012
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Based on the medals won by United States in 28 times Summer Olympic Games from 1896 to 2020, this study predicts the number of medals won by United States in 2024 and 2028 Summer Olympic Games. Considering that the host effect will produce "outlier data" on the number of medals and affect the prediction model, this paper constructs a robust threeparameter exponential smooth prediction model by combining BOX-COX transform with three-parameter exponential smooth prediction model. Through the prediction test on the number of gold medals and total medals in 2016 and 2020 Olympic Games of the United States, it is found that the robust prediction method is better than the traditional prediction method significantly, and the relative error is less than 5.13%. In view of this, the robust prediction model is applied to predict the number of gold medals and total number of medals in 2024 and 2028 Summer Olympics of the United States. The forecast results show that the number of gold medals in 2024 and 2028 is 41 and 39 respectively, and the total number of medals is 121 and 109 respectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhijun He and Zhijian Wang "Prediction of olympic medal count for USA based on robust time series model and computer implementation", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131845E (5 July 2024); https://doi.org/10.1117/12.3033012

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