A Network Traffic Prediction Method Based on LSTM Model of Attention Mechanism

Abstract

The present invention provides a network traffic prediction method based on an attention mechanism-based LSTM model, the method comprising the following steps: Step 1: data preprocessing, performing standardized processing on the network traffic data, and then dividing the network traffic data into training data and test data; Step 2: build a model, build an LSTM model based on the attention mechanism, step 3: model training, input training data into the LSTM model based on the attention mechanism, perform iterative training based on the Adam optimization algorithm, and obtain training Good model; Step 4: Network traffic forecasting. When calculating the output state of the current moment, the network traffic prediction method comprehensively considers the hidden states of the previous multiple moments, so that the generation probability of each item in the output sequence is affected by the hidden states of multiple input historical sequences. more precise.

Description

technical field [0001] The invention belongs to the technical field of network traffic prediction, in particular to a network traffic prediction method based on an attention mechanism LSTM model. Background technique [0002] With the rapid development of Internet technology, electronic products such as mobile phones and tablet computers have gradually penetrated into people's lives, and various network applications are more widely used by everyone. At the same time, the scale of network data is also growing, which puts forward higher requirements on the security, efficiency and stability of network equipment. How to do a good job in network planning and resource allocation has become a difficult problem faced by network administrators. Network traffic prediction can predict the network traffic value in a period of time in the future based on the historical network traffic data, effectively help network administrators to deal with network congestion problems, reasonably all...

AI Technical Summary

Problems solved by technology

Unable to capture the importance of different historical time series for the current time series output

[0006] 2. When the length of the historical sequence is long, the earlier processed historical sequence in LSTM is more likely to be forgotten, resulting in information loss and affecting model performance

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