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