Comparison of Parametric and Nonparametric Techniques for Non-peak Traffic Forecasting

نویسندگان

  • Yang Zhang
  • Yuncai Liu
چکیده

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. Keywords—Parametric and Nonparametric Techniques, Non-peak Traffic Forecasting

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تاریخ انتشار 2009