Research on Multidimensional Data Processing and Prediction Based on ARIMA Model and Principal Component Analysis

The focus of this study is the processing and analysis of race data, and employs algorithms such as ARIMA model and Principal Component Analysis (PCA) to construct a series of data analysis and prediction models to more accurately assess the influence of features in the data and make outcome predictions. First, preprocessing was performed for the raw data, including processing error values and missing values to ensure the completeness data accuracy. Then, the weights of each feature were calculated using the coefficient of variation method, and the results showed that the weights of the three most important features were 15.42%, 13.83%, and 11.27%, respectively. In terms of prediction, the ARIMA model for simulating the trend of data changes, the optimal ARIMA (0, 2, 1) model was determined, and the correlation between the prediction results and the actual data was verified. By principal component analysis (PCA), the accumulated contribution rate of the first four principal components reached 84.399%, and the eigenroot of principal component 4 was less than 1.0. This study demonstrates the application of machine learning algorithms in complex data analysis and trend prediction, and provides theoretical basis and technical support for further research in related fields.