Municipal revenue prediction by support vector machine ensembles

Authors:
Petr Hájek;Vladimír Olej
Affiliations:
Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Pardubice, Czech Republic;Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Pardubice, Czech Republic
Venue:
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume I
Year:
2010

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Abstract

Fiscal stress has forced municipalities to pay increasing attention to the importance of revenue prediction. Currently, econometric models and expert opinions are used for municipal revenue prediction. In this paper we present a design of support vector machine ensembles for the prediction of municipal revenue. Linear regression model and feed-forward neural network ensembles are used as benchmark methods. We prove that stochastic gradient boosting outperforms the other methods when creating SVM ensembles for this regression problem. Further, bagging shows best performance for feed-forward neural network ensembles, and dagging is preferable for linear regression model ensembles.