Predicting scoring trends in basketball games based on multivariate time series analysis

Abstract

Abstract In this paper, we first study the evaluation framework of Granger causality between time series. Then, the multivariate time series prediction model is constructed based on transfer entropy and graph attention network. The multivariate time series prediction model architecture has causal branches with transfer entropy, and the prediction module is executed using graph attention network. Finally, the accuracy of basketball game score prediction is analyzed, as well as comparing the accuracy of the multivariate time series prediction model with other models. The number of offensive rounds in basketball games can be predicted with a high accuracy of 67.94% using the multivariate time series prediction model. For the prediction of the scoring trend of player rotation, the ABHJI combination rotating on the court is the most efficient in scoring, and the average value of scoring is as high as 85.463. The average accuracy of the TSP reaches 72.2%, and the model has high feasibility and is suitable for scoring prediction in basketball games.