Long Horizon End-to-End Delay Forecasts: A Multi-Step-Ahead Hybrid Approach
Long Horizon End-to-End Delay Forecasts: A Multi-Step-Ahead Hybrid Approach
复制标题
长期端到端延迟预测：多步提前混合方法
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
A. Botta
中科院分区：
文献类型：
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作者：
Vinh T. Bui;Weiping Zhu;A. Pescapé;A. Botta
如果可能的话，长期的端到端延迟预测将是交通工程的突破。本文介绍了一种结合神经网络和模式识别技术的小波变换预测端到端延迟的混合方法。采用离散小波变换将延迟时间序列分解为一组小波分量，该小波分量由一个近似分量和若干细节分量组成。从而将长视界延迟预测问题转化为一组短视界小波系数预测问题。利用递归多层感知器神经网络预测小波近似分量的系数，以表征时滞序列的变化趋势。利用k近邻技术对反映背景交通突发性的小波细节分量的系数进行预测。该方法已在模拟和实际异构网络中得到验证，在平均归一化均方根误差方面显示出令人满意的结果。此外，与一些现有的和已知的方法相比，它表现出更优越的性能。
A long horizon end-to-end delay forecast, if possible, will be a breakthrough in traffic engineering. This paper introduces a hybrid approach to forecast end-to-end delays using wavelet transforms in combination with neural network and pattern recognition techniques. The discrete wavelet transform is implemented to decompose delay time series into a set of wavelet components, which is comprised of an approximate component and a number of detail components. Thus, it turns the problem of long horizon delay forecasting into a set of shorter horizon wavelet coefficient forecasting problems. A recurrent multi-layered perceptron neural network is applied to forecast coefficients of the wavelet approximate component, which represents the trend of the delay series. The k-nearest neighbors technique is used to forecast coefficients of the wavelet detail components, which reflect the burstiness of background traffic. The proposed approach has been verified in both simulation and over real heterogeneous networks showing promising results in terms of averaged normalized root mean square error. In addition, when compared to some existing and well known approaches it presents the superior performance.