Peak Traffic Prediction Using Nonparametric Approaches

Abstract:

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How to accurately predict peak traffic is difficult for various forecasting models. In this paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the technique and analyze the forecast performance in the domain. For comparison purpose, other two non-parametric predictors 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.

Info:

Periodical:

Advanced Materials Research (Volumes 378-379)

Edited by:

Brendan Gan, Yu Gan and Y. Yu

Pages:

196-199

DOI:

10.4028/www.scientific.net/AMR.378-379.196

Citation:

Y. Zhang "Peak Traffic Prediction Using Nonparametric Approaches", Advanced Materials Research, Vols. 378-379, pp. 196-199, 2012

Online since:

October 2011

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Price:

$35.00

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