Research on network traffic prediction based on Transformer
Network traffic prediction is one of the key tasks to be solved urgently in the field of network traf-fic analysis.Most of the current prediction methods based on machine learning ignore the long correlation of traffic and take a long time to process large amounts of data.In response to the above problems,the study uses Transformer for network traffic prediction,captures the long-range sequence relationship of traffic through a multi-head attention mechanism,and learns the global dependency of traffic.The experiment re-sults show that this method can improve the prediction accuracy and effectively reduce the training time.
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