Network traffic prediction based on FARIMA-GARCH model - Shuang-mao YANG; Wei GUO; Wei TANG (Tongxin xuebao)
The volatility and self-similarity features of network traffic poses great cha lenge to network traffic prediction. For this purpose, a novel network traffic prediction scheme based on FARIMA-GARCH model was formulated. A novel method was used to get a zero-mean traffic series by a piecewise two-way CUSUM detection algorithm. Then the fraction difference order was evaluated with precision by the presented bounded search method. After obtaining the model para-meters, the prediction algorithm was conducted by using FARIMA-GARCH model. Compared with the traditional me-thod, the limited search method reduces the evaluated error despite a slight computational cost. Then simulation was car-ried out to verify the accuracy of proposed algorithm with real network traffic. The proposed prediction method keeps the same time complexity with the FARIMA model prediction method, and the simulation result shows that the root mean-square error and relative root mean-square error, which closely resemble the RBF prediction method, is less than FARIMA model prediction method. And the interval estimation and volatility prediction performance is excellent.