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.

Abstract-The scale of higher education is an essential link in the process of the formulation of education planning and reasonable allocation of teaching resources. At the same time, it also provides the required basis and support for the government to formulate educational planning and policy. The scale of higher education development is influenced not only by the level of economic development and industrial structure, but also by the total population and the living standards of residents. We take these elements as the influence factors, which contain noise information. Because the scale of higher education and its impact factors have complex nonlinear relationship, the traditional forecasting method cannot describe their changing trends, which leads to the low accuracy of prediction. In order to solve the above problems, this paper bases on the traditional GM (1,1) model to judge the number of students in the future, and uses the weakening buffer operator to amend the historical data. Secondly, this paper analyzes the structure of the system cloud gray forecasting model, and demonstrates its integral generation principle. We propose a new method for the cosine gray forecasting model which is based on the system cloud�SCOS-GM (1, 1), and prove the effectiveness of SCOS-GM (1, 1) model by the residual test. Finally, the SCOS-GM (1, 1) model is utilized to predict the scale of higher education in China during the period of 2012-2014. The results show that the scale of higher education will demonstrate a gradual upward trend in the next few years.