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An improved echo state network based on variational mode decomposition and bat optimization for Internet traffic forecasting

机译：一种基于变分分解和蝙蝠优化的改进回声状态网络用于互联网流量预测

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摘要

Internet traffic forecasting plays an important role in network resource allocation, planning and anomaly detection. Developing an accurate and robust Internet traffic forecasting model is very essential. However, Internet traffic usually exhibits nonlinear and multi-scale data character. Thus, Internet traffic forecasting is also a huge challenge. Echo state network (ESN) is a novel time series forecasting approach and it is able to approximate any complex nonlinear relationship. Considering the fact that the standard ESN easily suffers from the influences of initial random weights, this paper employs bat algorithm (BA) to overcome this drawback. In addition, variational mode decomposition (VMD) is utilized to decompose the original Internet traffic series into several band-limited intrinsic mode functions (BLIMFs) for capturing the multi-scale data character. Based on decomposition result, the training set is reconstructed. Verified by four data sets, the proposed model defeats six popular forecasting methods. The simulation results indicate that the proposed model is an effective and robust alternative for Internet traffic forecasting.
机译：Internet流量预测在网络资源分配，规划和异常检测中起着重要作用。开发准确而强大的Internet流量预测模型非常重要。但是，Internet流量通常表现出非线性和多尺度数据特征。因此，互联网流量预测也是一个巨大的挑战。回声状态网络（ESN）是一种新颖的时间序列预测方法，它能够近似任何复杂的非线性关系。考虑到标准ESN容易受到初始随机权重的影响，本文采用蝙蝠算法（BA）克服了这一缺点。此外，利用变分模式分解（VMD）将原始Internet通信量序列分解为几个频带受限的固有模式函数（BLIMF），以捕获多尺度数据特征。根据分解结果，重建训练集。通过四个数据集验证，该模型击败了六种流行的预测方法。仿真结果表明，所提出的模型是一种有效且健壮的Internet流量预测替代方案。

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