%0 Journal Article
	%A Yang Zhang and  Yuncai Liu
	%D 2009
	%J International Journal of Mathematical and Computational Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 27, 2009
	%T Comparison of Parametric and Nonparametric Techniques for Non-peak Traffic Forecasting
	%U https://publications.waset.org/pdf/1000
	%V 27
	%X Accurately predicting non-peak traffic is crucial to
daily traffic for all forecasting models. In the paper, least squares
support vector machines (LS-SVMs) are investigated to solve such a
practical problem. It is the first time to apply the approach and analyze
the forecast performance in the domain. For comparison purpose, two
parametric and two non-parametric techniques are selected because of
their effectiveness proved in past research. Having good
generalization ability and guaranteeing global minima, LS-SVMs
perform better than the others. Providing sufficient improvement in
stability and robustness reveals that the approach is practically
promising.
	%P 172 - 178