包容性检验和SVM相融合的网络流量预测

模型选择对网络流量组合预测结果至关重要，为了提高网络流量的预测效果，提出一种包容性检验和支持向量机相融合的网络流量预测模型（ET-SVM）。采用多个单一模型对网络流量进行预测，根据预测结果的均方根误差对模型优劣进行排序，通过包容性检验，根据t统计量检验选择最合适的单一模型，采用支持向量机对单一模型预测结果进行组合得到最终预测结果，通过仿真实验对模型性能进行测试。仿真结果表明，ET-SVM降低了网络流量的预测误差，预测精度得到了提高。

Network traffic combination forecasting based on encompassing tests and Support Vector Machine

Model selection is a key problem for combination model of network traffic, and in order to improve the forecasting accuracy of network traffic, this paper proposes a network flow combination model based on encompassing test and Support Vector Machine. A lot of single models are used to forecast the network traffic, and the merits of the model are defined by mean square error of the forecasting results, and then the appropriate single model is selected by encompassing test, and the single model prediction results are combined by Support Vector Machine to get the final forecasting result of network traffic, and the performance of model is tested by the simulation experiment. The simulation results show that the proposed model can reduce the forecasting error and has improved the forecasting accuracy of network traffic.