关键词: 网络流量, 差分自回归滑动平均, 最小二乘向量机, 小波变换, 组合预测

Abstract: In order to improve predict accuracy, a combination prediction model of network traffic is proposed based on wavelet decomposition（WA-ARIMA-LSSVM）. In view of the network flow multi-scale characteristic, the network traffic time series is decomposed, and then autoregressive moving average models is used to prediction high frequency of for network traffic which low frequency is prediction by least squares support vector machine, finally high frequency and low frequency results are reconstructed, and model performance is tested by simulation experiment. The results show that compared with other network traffic prediction models, WA-ARIMA-LSSVM can accurately reflect the complex change trends of network traffic and improves the prediction accuracy of network traffic.

Key words: network traffic, Autoregressive Integrated Moving Average Model（ARIMA）, Least Squares Support Vector Machines（LSSVM）, wavelet transform, combination prediction