%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