Application of ARIMA model in classification and prediction of athlete training data

Abstract

Traditional athlete training data classification and prediction models have low accuracy, poor processing of high-dimensional data, and weak dynamic adaptability. Before applying the ARIMA model for classification and prediction, the first step is to use an automated data acquisition system to connect sports devices, collect and clean athlete training data in real time, and securely store and backup it on a central server. The second step is to preprocess the collected data and divide it into training and test sets to ensure the accuracy and generalization ability of the model. The third step is to conduct time-series analysis to identify the time-dependent and seasonal components of athlete training data. The fourth step involves fitting the ARIMA model through differential analysis, stationarity testing, model parameter optimization, residual analysis, rolling forecasting, and ensemble learning, and predicting and classifying athlete training data, so as to improve the accuracy and robustness of the model. The experimental results show that the accuracy of data classification using ARIMA model is the highest, all exceeding 92%, and the average classification accuracy is 2%–16.7% higher than that of other models. Moreover, the prediction errors of the ARIMA model are all below 1.0%. In summary, the application of ARIMA models to classification and prediction of the athlete training data is highly reliable.