@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},
	}