Application of Grey Forecasting Model Based on Improved Residual Correction in the Cost Estimation of University Education

Abstract

Abstract-The forecast of the cost of education in university is conducive to strengthening the management of the cost of education, mining the potential of reducing the cost, improving the management level and improving the use efficiency of the funds. Through accounting and forecasting of the cost of education in university, we can make the school to plan the cost and quota index according to its own practical needs, so as to improve the financial system and cost management system of university education. This will enable the university to carry out the correct decision-making, and provide support for the preparation of financial budget and long-term planning. At present, there are some defects in existing method of the university education cost prediction. The unitary regression method is very difficult to effectively remove the noise value in the fitting. Artificial neural network model is applied to predict the big data. Although the traditional gray forecasting model has a good prediction effect with the less data and poor information, the model still has the disadvantage that the background value is not smooth enough. In order to solve the above problems, this paper proposes an adaptive residual correction method based on grey system theory, and improves the grey forecasting model. This method can effectively remove the noise in the original data sequence, and it can be used to predict the cost of university education in China.

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

“…Articles dealing with profiling and prediction were classified into three sub-categories; admission decisions and course scheduling (n = 7), drop-out and retention (n = 23), and student models and academic achievement (n = 27). One study that does not fall into any of these categories is the study by Ge and Xie (2015), which is concerned with forecasting the costs of a Chinese university to support management decisions based on an artificial neural network. All of the 58 studies in this area applied machine learning methods, to recognise and classify patterns, and to model student profiles to make predictions.…”