Abstract
This paper describes a Decision Support System to provide indicators to support budget plan decisions, in a local government organization, the municipality of Lagoa - S. Miguel, Azores. The work includes system modeling, using the UML notation, the development of a MySQL relational database, algorithms for data collection using PHP, and forecasting models using R functions, such as exponential smoothing, classical decomposition with linear trend, and ARIMA models. Users have access to predictions made by different models for several indicators, being suggested to use the models with closest to zero errors. From the analysis performed considering 12 years data, it is concluded that for most of indicators, the classical decomposition model is the most successful. However, for some indicators, it was found that the two error measures used are not consistent. In these cases, the final decision is left to the decision-maker, taking advantage of his domain knowledge.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Valacich, J., Schneider, C.: Information Systems Today. Prentice Hall (2013)
Burstein, F., Holsapple, C.: Handbook on Decision Support Systems. Springer (2008)
Power, D.J.: Decision Support Systems: Concepts and Resources for Managers. Greenwood/Quorum, Westport (2002)
Mendes, A., Alfaro, P., Ferreira, A.: Business Intelligence no suporte a decisão sobre comunicações: descrição de um caso. Revista de Ciências da Computação (2008)
Lopes, F., Morais, M., Carvalho, A.: Desenvolvimento de Sistemas de informação. FCA (2009)
Mendes, A.B., Themido, I.H.: Multi-outlet retail site location assessment. International Transactions in Operational Research 11(1), 1–18 (2004)
Ramos, P.: Desenhar Bases de dados com UML. Edições Sílabo (2012)
Hyndman, R.: Forecast: Forecasting functions for time series and linear models. Repositório CRAN (2014)
Armstrong, J.: Principles of forecasting. Kluwer Academic Publishers (2001)
Hyndman, R., Maxwell, L., Pitrun, I., Billah, B.: Local linear forecasts using cubic smoothing splines, University of Australia – Department of Economics (2002)
Hyndman, R.J., Kostenko, A.V.: Minimum Sample Size requirements for Seasonal Forecasting Models. Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters 6, 12–15 (2007)
Schapire, R.E.: The Strength of Weak Learnability. Machine Learning 5(2), 197–227 (1990)
Cortes, B.: Sistemas de Suporte à Decisão. FCA (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Rego, H., Mendes, A.B., Guerra, H. (2015). A Decision Support System for Municipal Budget Plan Decisions. In: Rocha, A., Correia, A., Costanzo, S., Reis, L. (eds) New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 354. Springer, Cham. https://doi.org/10.1007/978-3-319-16528-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-16528-8_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16527-1
Online ISBN: 978-3-319-16528-8
eBook Packages: EngineeringEngineering (R0)