Enhancing Efficiency in Public Education Expenditure through Short-Term Forecasting

Autores

  • Hebert Wesley Pereira Zaroni Universidade Federal de Itajubá https://orcid.org/0009-0007-0378-1382
  • Rafael de Carvalho Miranda Universidade Federal de Itajubá https://orcid.org/0009-0007-0378-1382
  • Alexandre Ferreira de Pinho Universidade Federal de Itajubá
  • Arthur Leandro Guerra Pires Universidade Federal de Itajubá

DOI:

https://doi.org/10.4025/actascitechnol.v48i1.72801

Palavras-chave:

Higher education; efficiency; public expenditure; forecasting.education; efficiency; public expenditure; forecasting.

Resumo

This study presents a short-term forecast of the financial expenses of a Federal Institution of Higher Education (HEI) within the context of budgetary challenges in Brazil, especially in the field of public education. Based on time series data of HEI expenditures, the SARIMA model is employed to forecast the financial needs for the upcoming fiscal months. The results demonstrate the potential of more accurate forecasts to optimize the efficiency of public spending in Brazil’s educational system, as well as to support operational planning, including workforce allocation. One limitation of this study is that future research could explore some additional variables or techniques to increase the accuracy and robustness of the forecasts. Nevertheless, the findings have the potential to significantly contribute to the improvement of financial planning and resource management in HEIs, promoting a more effective allocation of resources to meet the needs of the local academic community and, consequently, increasing the efficiency of public spending in Brazil. Enhancing financial forecasting in HEIs can make it possible to achieve efficient resource allocation and greater transparency and accountability in public spending. By employing the SARIMA model for short-term financial forecasting, this study offers an innovative solution to improve financial requirements. This approach stands out by providing a data-driven method linked to the unique context of HEIs in Brazil, offering insights for improving operational efficiency.

Downloads

Não há dados estatísticos.

Referências

Albassam, B. A. (2020). A model for assessing the efficiency of government expenditure. Cogent Economics & Finance, 8(1), Artigo 1823065. https://doi.org/10.1080/23322039.2020.1823065

Arunraj, N. S., & Ahrens, D. (2015). A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting. International Journal of Production Economics, 170, 321–335. https://doi.org/10.1016/j.ijpe.2015.09.014

Arvan, M., Fahimnia, B., Reisi, M., & Siemsen, E. (2019). Integrating human judgement into quantitative forecasting methods: A review. Omega, 86, 237–252. https://doi.org/10.1016/j.omega.2018.07.012

Assunção, C. S. D. L. T., Vieira, M. M., & Ho, L. L. (2020). The use of control chart for a continuous monitoring of the water-oil ratio in fields of the Potiguar basin/Brazil. Acta Scientiarum. Technology, 42, Artigo e44358. https://doi.org/10.4025/actascitechnol.v42i1.44358

Bacci, L. A., Mello, L. G., Incerti, T., de Paiva, A. P., & Balestrassi, P. P. (2019). Optimization of combined time series methods to forecast the demand for coffee in Brazil: A new approach using Normal Boundary Intersection coupled with mixture designs of experiments and rotated factor scores. International Journal of Production Economics, 212, 186–211. https://doi.org/10.1016/j.ijpe.2019.02.015

Benito, B., Faura, U., Guillamón, M. D., & Ríos, A. M. (2019). The efficiency of public services in small municipalities: The case of drinking water supply. Cities, 93, 95–103. https://doi.org/10.1016/j.cities.2019.04.014

Boueri, R., Rocha, F. F., & Rodopoulos, F. M. A. (Orgs.). (2015). Avaliação da qualidade do gasto público e mensuração da eficiência. Secretaria do Tesouro Nacional.

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5ª ed.). John Wiley & Sons.

Chacón, H., Koppisetti, V., Hardage, D., Choo, K. K. R., & Rad, P. (2023). Forecasting call center arrivals using temporal memory networks and gradient boosting algorithm. Expert Systems with Applications, 224, Artigo 119983. https://doi.org/10.1016/j.eswa.2023.119983

Choi, T. M., Yu, Y., & Au, K. F. (2011). A hybrid SARIMA wavelet transform method for sales forecasting. Decision Support Systems, 51(1), 130–140. https://doi.org/10.1016/j.dss.2010.12.002

Divino, J. A., Maciel, D. T., & Sosa, W. (2020). Government size, composition of public spending and economic growth in Brazil. Economic Modelling, 91, 155–166. https://doi.org/10.1016/j.econmod.2020.06.001

dos Santos, C. H., Lima, R. D. C., Leal, F., de Queiroz, J. A., Balestrassi, P. P., & Montevechi, J. A. B. (2020). A decision support tool for operational planning: A Digital Twin using simulation and forecasting methods. Production, 30, Artigo e20200017. https://doi.org/10.1590/0103-6513.20200017

Fakharzadeh, T. (2016). Budgeting and accounting in OECD education systems: A literature review (OECD Education Working Papers, No. 128). OECD Publishing. https://doi.org/10.1787/5jm3xgsz03kh-en

Fildes, R., Ma, S., & Kolassa, S. (2022). Retail forecasting: Research and practice. International Journal of Forecasting, 38(4), 1283–1318. https://doi.org/10.1016/j.ijforecast.2021.11.001

Giacomoni, J. (2021). Orçamento público (18ª ed.). Atlas.

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2ª ed.). OTexts. https://otexts.com/fpp2/

Karaev, A. K., Gorlova, O. S., Sedova, M. L., Ponkratov, V. V., Shmigol, N. S., & Demidova, S. E. (2022). Improving the accuracy of forecasting the TSA daily budgetary fund balance based on wavelet packet transforms. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), Artigo 107. https://doi.org/10.3390/joitmc8030107

Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003

Leite Coelho da Silva, F., da Costa, K., Canas Rodrigues, P., Salas, R., & López-Gonzales, J. L. (2022). Statistical and artificial neural networks models for electricity consumption forecasting in the Brazilian industrial sector. Energies, 15(2), Artigo 588. https://doi.org/10.3390/en15020588

Liu, C., Xu, Z., Zhao, K., & Xie, W. (2023). Forecasting education expenditure with a generalized conformable fractional-order nonlinear grey system model. Heliyon, 9(6), Artigo e16701. https://doi.org/10.1016/j.heliyon.2023.e16701

Liu, X., Lin, Z., & Feng, Z. (2021). Short-term offshore wind speed forecast by seasonal ARIMA-A comparison against GRU and LSTM. Energy, 227, Artigo 120492. https://doi.org/10.1016/j.energy.2021.120492

Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (2008). Forecasting methods and applications (3ª ed.). John Wiley & Sons.

Mancuso, A. C. B., & Werner, L. (2019). A comparative study on combinations of forecasts and their individual forecasts by means of simulated series. Acta Scientiarum. Technology, 41, Artigo e41452. https://doi.org/10.4025/actascitechnol.v41i1.41452

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting (2ª ed.). John Wiley & Sons.

Moon, H., Lee, H., & Song, B. (2022). Mixed pooling of seasonality for time series forecasting: An application to pallet transport data. Expert Systems with Applications, 201, Artigo 117195. https://doi.org/10.1016/j.eswa.2022.117195

Nayeri, S., Khoei, M. A., Rouhani-Tazangi, M. R., GhanavatiNejad, M., Rahmani, M., & Tirkolaee, E. B. (2023). A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system. Engineering Applications of Artificial Intelligence, 124, Artigo 106511. https://doi.org/10.1016/j.engappai.2023.106511

Niu, T., Wang, J., Lu, H., Yang, W., & Du, P. (2020). Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting. Expert Systems with Applications, 148, Artigo 113237. https://doi.org/10.1016/j.eswa.2020.113237

Paludo, A. (2017). Orçamento público, AFO e LRF (7ª ed.). Método.

Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B., ... & Ziel, F. (2022). Forecasting: Theory and practice. International Journal of Forecasting, 38(3), 705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001

Portal da Transparência do Governo Federal. (s.d.). Página inicial. Controladoria-Geral da União. Recuperado em 13 de março de 2026, de https://portaldatransparencia.gov.br/

Savun-Hekimo?lu, B., Erbay, B., Hekimo?lu, M., & Burak, S. (2021). Evaluation of water supply alternatives for Istanbul using forecasting and multi-criteria decision making methods. Journal of Cleaner Production, 287, Artigo 125080. https://doi.org/10.1016/j.jclepro.2020.125080

Simian, D., Stoica, F., & B?rbulescu, A. (2020). Automatic optimized support vector regression for financial data prediction. Neural Computing and Applications, 32, 2383–2396. https://doi.org/10.1007/s00521-018-3814-1

Sinuany-Stern, Z. (2021). Forecasting methods in higher education: An overview. Em Handbook of Operations Research and Management Science in Higher Education (pp. 131–157). Springer. https://doi.org/10.1007/978-3-030-74051-1_5

Villegas, M. A., & Pedregal, D. J. (2019). Automatic selection of unobserved components models for supply chain forecasting. International Journal of Forecasting, 35(1), 157–169. https://doi.org/10.1016/j.ijforecast.2018.09.006

Wang, C. C. (2011). A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan export. Expert Systems with Applications, 38(8), 9296–9304. https://doi.org/10.1016/j.eswa.2011.01.108

Wang, X., Kang, Y., Hyndman, R. J., & Li, F. (2023). Distributed ARIMA models for ultra-long time series. International Journal of Forecasting, 39(3), 1163–1184. https://doi.org/10.1016/j.ijforecast.2022.05.001

Wright, J. H. (2019). Some observations on forecasting and policy. International Journal of Forecasting, 35(3), 1186–1192. https://doi.org/10.1016/j.ijforecast.2019.01.001

Wu, K., Xu, C., Yan, J., Wang, F., Lin, Z., & Zhou, T. (2023). Error-distribution-free kernel extreme learning machine for traffic flow forecasting. Engineering Applications of Artificial Intelligence, 123, Artigo 106411. https://doi.org/10.1016/j.engappai.2023.106411

Downloads

Publicado

2026-04-14

Como Citar

Zaroni, H. W. P. ., Miranda, R. de C. ., Pinho, A. F. de ., & Pires , A. L. G. . (2026). Enhancing Efficiency in Public Education Expenditure through Short-Term Forecasting. Acta Scientiarum. Technology, 48(1), e72801. https://doi.org/10.4025/actascitechnol.v48i1.72801

Edição

Seção

Estatí­stica

Artigos mais lidos pelo mesmo(s) autor(es)