Aiming at chaotic characteristics of network traffic and the shortage of traditional forecasting models

In order to improve forecasting accuracy of network traffic

a new network traffic prediction model based on chaos theory

wavelet analysis and relevance vector machine is proposed in this paper. Firstly

wavelet analysis is used to decompose the network traffic and the components of different frequency characteristics are obtained

and secondly

phase space reconstruction of components is carried out by using chaos theory

lastly

wavelet analysis is used to combine and get the final results of network traffic and network traffic is used to do simulation experiment. The results show that the compared with other models

the proposed model can accurately reflect the chaotic characteristics of the network traffic

and obtain the higher accuracy of the prediction results.