基于HP滤波的ARMA-ABCSVR-GABP网络流量预测

针对当前网络流量无法根据流量变化的特征进行预测,且通过单一或者组合模型依然得不到较高准确率的问题,提出一种基于HP(High-Pass Fliter)滤波的流量预测模型.基于高铁站流量数据日高夜低的周期特性以及流量波动增长的长期趋势,依据HP滤波将网络流量分解成周期序列及趋势序列.利用自回归-滑动平均模型(ARMA)对...

ARMA-ABCSVR-GABP NETWORK TRAFFIC PREDICTION BASED ON HP FILTER

In order to solve the problem that the current network traffic cannot be predicted according to the characteristics of traffic changes,and the high accuracy still cannot be obtained through a single or combined model,a traffic prediction model based on HP filtering is proposed.Based on the periodic characteristics of high daytime and low nighttime traffic data in high-speed railway station and the long-term trend of traffic fluctuation growth,the network traffic was decomposed into periodic sequence and trend sequence according to HP filtering.The advantage of ARMA in predicting stationary sequence was used to predict periodic change.The SVR optimized by ABC was used to predict trend sequence.Finally,the prediction results of the two were superimposed,and GABP was used to further improve the accuracy.The results show that the prediction method is reliable.Compared with other methods,the superiority of this method is proved.