A network traffic prediction method and device

Abstract

The embodiment of the invention provides a network flow prediction method and device, and relates to the technical field of networks. The method comprises the steps of determining the prediction moment of network flow; determining a first feature vector of the network flow at the prediction moment according to the network flow information at the first historical moment, wherein the first feature vector comprises a first flow feature and a change feature of the first flow feature; inputting the first feature vector into a network flow prediction model, and obtaining a flow prediction value at aprediction moment, wherein the network traffic prediction model is model obtained by training a preset regression model by adopting a sample feature vector and a flow actual value of the sample network flow at a sample moment; the sample eigenvector is an eigenvector determined according to the network flow information at a second historical moment, the sample moment is an actual generation moment of the sample network flow, and the sample eigenvector comprises a sample flow characteristic and a change characteristic of the sample flow characteristic. By applying the scheme provided by the embodiment of the invention, the accuracy of network flow prediction is improved.

Description

technical field [0001] The present application relates to the field of network technology, in particular to a network traffic prediction method and device. Background technique [0002] With the rapid development of Internet technology, the Internet has penetrated into all aspects of people's work and life. In the process of network management, network traffic is of great importance to network capacity planning, network equipment design, network resource allocation, load balancing, etc. Therefore, it is necessary to predict network traffic in practical applications. [0003] During the process of implementing the present application, the inventors found at least the following problems in the prior art. [0004] In the prior art, when predicting network traffic, AR (Auto-Regressive Model, autoregressive model), MA (Moving Average Model, moving average model), ARMA (Auto-Regressive and Moving Average Model, autoregressive moving average Model), ARIMA (Autoregressive Integrat...

AI Technical Summary

Problems solved by technology

Although network traffic prediction can be realized by applying this method, since this method mainly uses linear mapping, and linear mapping is generally used to describe short-term time series, that is to say, linear mapping is mainly used to predict short-term network traffic, therefore, tends to lead to poor accuracy of predicted network traffic

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