Hybrid models combining neural networks and nonparametricregression models used for time series prediction

Authors:
Dursun Aydin;Mammadagha Mammadov
Affiliations:
Department of Statistics, Muǧla University, Kötekli, Muǧla, Turkey;Department of Statistics, Anadolu University, Tepebasi, Eskisehir, Turkey
Venue:
ISTASC'09 Proceedings of the 9th WSEAS International Conference on Systems Theory and Scientific Computation
Year:
2009

Citing 5
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Abstract

In this paper, we proposed the hybrid models whose components are nonparametric regression and artificial neural networks. Smoothing spline, regression spline and additive regression models are considered as the nonparametric regression components. Furthermore, various multilayer perceptron algorithms and radial basis function network model are regarded as the artificial neural networks components. The performances of the models have been compared for the number of cars produced in Turkey. The results obtained by experimental evaluations show that hybrid models proposed in this study have performed much better in comparison to hybrid models examined by others (see for example, [1] and [2]).