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A Study on Olympic Medal Table Prediction Based on LSTM and DBILSTM
Abstract
This paper focuses on the prediction of Olympic medals list, using Long Short-Term Memory (LSTM) and two-order bi-directional long short-term memory neural network (DBILSTM) models. The study collates and cleans the data related to several Olympic Games, selects 10 variable indicators such as the gender of athletes, consecutive awards, the proportion of national participation and awards, etc., and applies the LSTM model to predict the number of medals of some countries in 2028, and passes the tests of MAE, RMSE, and MAPE indexes, and the results show that the model accuracy is good. Meanwhile, the innovative DBILSTM model is used to effectively integrate the information before and after the sequence to calculate the probability of multiple countries winning the first medal in 2028. The results of this research provide valuable references for countries to formulate sports development strategies and commercial layout of events, and also contribute new methods and ideas to the field of sports event forecasting.
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