Prediction of university fund revenue and expenditure based on fuzzy time series with a periodic factor.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2023
Historique:
received: 12 12 2022
accepted: 15 05 2023
medline: 29 5 2023
pubmed: 25 5 2023
entrez: 25 5 2023
Statut: epublish

Résumé

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.

Identifiants

pubmed: 37228158
doi: 10.1371/journal.pone.0286325
pii: PONE-D-22-34040
pmc: PMC10212173
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0286325

Informations de copyright

Copyright: © 2023 Shen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

MethodsX. 2020 Jul 29;7:101015
pubmed: 32793431

Auteurs

Yueqian Shen (Y)

Department of Finance and Asset Management, China University of Geosciences, Wuhan, China.

Xiaoxia Ma (X)

Department of Finance and Asset Management, China University of Geosciences, Wuhan, China.

Yajing Sun (Y)

Audit Department, China University of Geosciences, Wuhan, China.

Sheng Du (S)

School of Automation, China University of Geosciences, Wuhan, China.
Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, China.
Shenzhen ET Technology Co., Ltd, Shenzhen, China.

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Classifications MeSH