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基于局域相关向量机回归模型的小尺度网络流量的非线性预测*
Nonlinear prediction of small scale network traffic based on local relevance vector machine regression model*
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摘要: 基于非线性时间序列局域预测法与相关向量机回归模型，本文提出了局域相关向量机预测方法，并应用于预测实际的小尺度网路流量序列。应用基于信息准则的局域预测法邻近点的选取方法来选取局域相关向量机回归模型的邻近点个数。对比分析了局域相关向量机预测法、前馈神经网络模型与局域线性预测法对网络流量序列的预测性能，其中前馈神经网络模型的参数采用粒子群优化算法来优化。实验结果表明：邻近点优化后的局域相关向量机回归模型能够有效地预测小尺度网络流量序列，归一化均方误差很小；局域相关向量机回归模型生成的时间序列具有与原网络流量时间序列相一致的概率分布；局域相关向量机回归模型的预测精度好于前馈神经网络模型的与局域线性预测法的。
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关键词:
- 小尺度网络流量 /
- 非线性时间序列预测方法 /
- 局域预测法 /
- 相关向量机回归模型
Abstract: Based on the nonlinear time series local prediction method and the relevance vector machine regression model, the local relevance vector machine prediction method is proposed and applied to predict the small scale traffic measurement data, and the BIC-based neigh-bor point selection method is used to choose the number of nearest-neighbor points for the local relevance vector machine regression model. We also compare the performance of the local relevance vector machine regression model with the feed-forward neural network optimized by particle swarm optimization for the same problem. Experimental results show that the local relevance vector machine prediction method whose neighboring points have been optimized can effectively predict the small scale traffic measurement data, can reproduce the statistical features of real small scale traffic measurements, and the prediction accuracy of the local relevance vector machine regression model is superior to that of the feedforward neural network optimized by PSO and the local linear prediction method. -
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