Data analysis of Higher Education System Based on TOPSIS and Time Series Model

Abstract

Building sustainable and healthy higher education has become a serious problem facing the world. In this article, we have selected 9 representative indicators as our factors in evaluating the higher education system. We developed an evaluation system based on the "Sequence of Preference Techniques Similar to the Ideal Solution" (TOPSIS). We use principal component analysis (PCA) to find the relationship between different feature variables. We apply our evaluation system to the higher education system in the United States and use PCA to analyze it. Then, we made some suggestions on the weaknesses of the PCA results. Finally, we use the seasonal autoregressive integrated moving average (SARIMA) model to simulate the changes after the policy is released In conclusion, we have built an effective higher education evaluation system, and the results of the evaluation system are positive for the development of the country. In addition, we found that if some relevant policies are introduced to improve the shortcomings of the higher education system, the ranking of the US higher education system will be greatly improved to the 12th place among 50 countries.