@article{(Open Science Index):https://publications.waset.org/pdf/1000, title = {Comparison of Parametric and Nonparametric Techniques for Non-peak Traffic Forecasting}, author = {Yang Zhang and Yuncai Liu}, country = {}, institution = {}, abstract = {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.}, journal = {International Journal of Mathematical and Computational Sciences}, volume = {3}, number = {3}, year = {2009}, pages = {172 - 178}, ee = {https://publications.waset.org/pdf/1000}, url = {https://publications.waset.org/vol/27}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 27, 2009}, }