In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the...In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.展开更多
网络流量时间序列具有高维度、非线性和时变性等特征,针对传统时间序列模型预测精度较低的问题,提出基于KPCA(Kernel Principal Component Analysis)优化IHS-RVM的小时间尺度网络流量预测模型。首先对网络流量时间序列进行相空间重构,...网络流量时间序列具有高维度、非线性和时变性等特征,针对传统时间序列模型预测精度较低的问题,提出基于KPCA(Kernel Principal Component Analysis)优化IHS-RVM的小时间尺度网络流量预测模型。首先对网络流量时间序列进行相空间重构,确定嵌入维数和延迟时间。然后利用KPCA对网络流量样本进行核主成分特征提取,降低嵌入维数,并获取核主元矩阵。在此基础上,通过改进HS(Harmony Search)算法(IHS)确定RVM核参数。最后利用参数优化的RVM模型进行小时间尺度网络流量预测。为了交叉验证模型的性能,采用实际数据进行性能对比分析。结果表明,本模型性能优于KPCA-IHS-ESN、KPCA-IHS-SVM和IHS-RVM模型,取得了良好的效果。展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
文摘网络流量时间序列具有高维度、非线性和时变性等特征,针对传统时间序列模型预测精度较低的问题,提出基于KPCA(Kernel Principal Component Analysis)优化IHS-RVM的小时间尺度网络流量预测模型。首先对网络流量时间序列进行相空间重构,确定嵌入维数和延迟时间。然后利用KPCA对网络流量样本进行核主成分特征提取,降低嵌入维数,并获取核主元矩阵。在此基础上,通过改进HS(Harmony Search)算法(IHS)确定RVM核参数。最后利用参数优化的RVM模型进行小时间尺度网络流量预测。为了交叉验证模型的性能,采用实际数据进行性能对比分析。结果表明,本模型性能优于KPCA-IHS-ESN、KPCA-IHS-SVM和IHS-RVM模型,取得了良好的效果。
