Paper Title:
Peak Traffic Prediction Using Nonparametric Approaches
  Abstract

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)
Chapter
Chapter 2: Structural and New Functional Materials
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
Authors
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Price
$32.00
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