How to accurately predict traffic data with weak regularity is difficult for various forecasting models. In this paper, least squares support vector machines (LS-SVMs) are proposed to deal with such a problem. It is the first time to apply the technique and analyze the forecast performance in the field. For comparison purpose, other three baseline 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.
A Novel Approach to Forecast Weakly Regular Traffic Status
2008-10-01
360011 byte
Conference paper
Electronic Resource
English
Online Contents | 1998
NTIS | 1967
Online Contents | 2001
|Unmanned aviation traffic forecast
Tema Archive | 2008
|Unmanned aviation traffic forecast
Online Contents | 2009
|