A Comparative Study of Hybrid, Neural Networks and Nonparametric Regression Models in Time Series Prediction

This paper presents a comparative study of the hybrid models, neural networks and nonparametric regression models in time series forecasting. The components of these hybrid models are consisting of the nonparametric regression and artificial neural networks models. 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 these models are compared by forecasting the series of number of produced Cars and Domestic product per capita (GDP) data occurred in Turkey. This comparisons show that hybrid models proposed in this paper have denoted much more excellent performance than the hybrid models in literature. Key-Words: Time series, Neural networks, Multilayer perceptrons, Radial basis function, Nonparametric regression, Additive regression model, Hybrid models.