Network traffic prediction method based on improved ESN

Abstract

The invention provides a network traffic prediction method based on an improved ESN. The method comprises the following steps: firstly, performing continuous collection on network traffic data, and then performing noise reduction processing on the original network traffic data on the basis of the network traffic data to obtain network traffic data subjected to the noise reduction processing; meanwhile, constructing a network traffic prediction model based on the improved ESN, combining the network traffic data subjected to the noise reduction processing with the original network traffic data to serve as input, and constructing a dual-ring reserve pool structure with a fixed structure to replace the random reserve pool structure of the original ESN; and finally, improving the ESN through training, and obtaining a network traffic prediction model based on the improved ESN that can be applied to network traffic prediction. By adoption of the method, the accuracy of a network traffic prediction result can be improved, and a better prediction effect can be obtained in nonlinear time series prediction.

Description

technical field [0001] The invention relates to a network flow prediction method based on an improved ESN, and belongs to the technical field of computer applications. Background technique [0002] Network plays a very important role in social life, enterprise production, operation and management. With the development of Internet technology, the scale of the network continues to expand, the complexity of the network is getting higher and higher, and people's requirements for network management are also getting higher and higher. Network traffic is an important parameter for evaluating network load and operating status. Continuous monitoring and accurate prediction of network traffic is an important means to grasp network operating status and realize effective management and control. Therefore, it is of great significance to study the prediction of network traffic. [0003] Network traffic has characteristics such as self-similarity, long-term correlation, periodicity and c...

AI Technical Summary

Problems solved by technology

In recent years, with the continuous expansion of network scale, traditional linear prediction methods such as AR, ARMA, and Poisson models have been unable to accurately describe the complex nonlinear relationship of network traffic.

With the in-depth study of network traffic, researchers found that although most of the network traffic prediction models based on traditional neural networks have relatively good prediction performance, and the model is simple and convenient, their performance is not stable enough.

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