Abstract
Financial management and decision-making of universities play an essential role in their development. Predicting fund revenue and expenditure of universities can provide a necessary basis for funds risk prevention. For the lack of solid data reference for financial management and funds risk prevention in colleges and universities, this paper presents a prediction model of University fund revenue and expenditure based on fuzzy time series with a periodic factor. Combined with the fuzzy time series, this prediction method introduces the periodic factor of university funds. The periodic factor is used to adjust the proportion of the predicted value of the fuzzy time series and the periodic observation value. A fund revenue prediction model and a fund expenditure prediction model are constructed, and an experiment is carried out with the actual financial data of a university in China. The experimental result shows the effectiveness of the proposed model, which can provide solid references for financial management and funds risk prevention in universities.
Funder
Education and Teaching Reform Special Fund for Central University
Fundamental Research Funds for the Central Universities
Publisher
Public Library of Science (PLoS)
Reference41 articles.
1. A Macro Analysis of the Investment Intensity, Structure and System of Education Funding——Based on the Comparison between China and OECD Countries;J Yu;Friends of Accounting,2020
2. On the Necessity of the Application of Cost Accounting in the Financial Management of Colleges and Universities: Taking A University as an Example;Y Q. Shen;Economic and Social Development Research,2021
3. Game Analysis and Suggestions on the Competitive Choice of Fund Deposit Management in Colleges and Universities;X. Yan;Educational Accounting Research,2021
4. Rahmat R F, Syaputra A H, Faza S, et al. Prediction of regional revenue and expenditure budget using autoregressive integrated moving average (ARIMA). IOP Conference Series: Materials Science and Engineering. IOP Publishing, 851 (1): 012064, 2020.
5. Alam T. Predicting revenues and expenditures using artificial neural network and autoregressive integrated moving average. Proceedings of the International Conference on Computing and Information Technology. IEEE, 1–4, 2020.