Combined network traffic prediction method based on ensemble empirical mode decomposition

Abstract

The invention belongs to the technical field of network traffic prediction, and particularly relates to a combined network traffic prediction method based on ensemble empirical mode decomposition, which comprises the following steps: obtaining original traffic data and preprocessing the original traffic data; decomposing the network flow into IMF components with single frequency on different timescales through ensemble empirical mode decomposition; determining the stationarity of the IMF component sequence through autocorrelation and partial autocorrelation analysis; predicting the stable IMFcomponent by using a linear ARMA model; predicting the non-stationary IMF component by using a nonlinear Elman neural network; summing the predicted values of the IMF component sequences to obtain apredicted value of the network traffic; According to the method, the actual network flow is described and predicted more accurately and comprehensively, so that the prediction precision is improved, and the prediction reliability is improved.

Description

Technical field [0001] The invention belongs to the technical field of network traffic prediction, and in particular relates to a combined network traffic prediction method based on Empirical Mode Decomposition (EMD). Background technique [0002] In recent years, with the rapid development of the Internet industry, the scale of the network has become increasingly large, the network structure has become increasingly complex, and network management has faced enormous challenges. Improving the efficiency of network operations through efficient network management mechanisms is an important topic for network management workers. [0003] Facing the increasingly complex network interconnection environment and increasing network traffic, researchers and scholars need to use more resources and time to monitor and analyze the situation of these network traffic to deal with the sudden situation of network congestion and congestion. To ensure good network quality. Traditional network manage...

AI Technical Summary

Problems solved by technology

However, wavelet transform has the problem of determining the number of decomposition layers and choosing wavelet bases, which depends on specific signal characteristics and application fields, and is not self-adaptive.

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