An Improved ARIMA-Neural Network Fusion Model for Multivariate Time-Series Prediction with External Factor Integration - You, Shaonan; Chen, Jing; Wei, Liang; Zhou, Shuyi (Transactions on Computer Science and Intelligent Systems Research, 2025)
This study aims to solve the difficult problem of sports performance prediction and improve the accuracy and reliability of prediction. The improved ARIMA and neural network fusion technology was used to clean and standardize the sports data first, and then the improved ARIMA model was used to capture the time series features, and external factors such as athletes’ physical fitness and training level were included in the model, and special cases were predicted by combining Bayes theory. Finally, neural network is used to further optimize the prediction results. The experimental results show that the fusion technology effectively integrates the advantages of the two models, and the prediction error is significantly reduced compared with the single model. The research conclusion is that the improved ARIMA and neural network fusion technology is feasible and effective in the field of sports performance prediction, which provides an innovative method for subsequent sports-related research and practice, and strongly promotes the development of sports data prediction technology.