文章基本信息

标题：NTAM-LSTM models of network traffic prediction
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作者：Jihong Zhao ; Xiaoyuan He
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2022
卷号：355
页码：1-10
DOI：10.1051/matecconf/202235502007
语种：English
出版社：EDP Sciences
摘要：Accurate prediction of network traffic is very important in allocating network resources. With the rapid development of network technology, network traffic becomes more complex and diverse. The traditional network traffic prediction model cannot accurately predict the current network traffic within the effective time. This paper proposes a Network Traffic Prediction Model----NTAM-LSTM, which based on Attention Mechanism with Long and Short Time Memory. Firstly, the model preprocesses the historical dataset of network traffic with multiple characteristics. Then the LSTM network is used to make initial prediction for the processed dataset. Finally, attention mechanism is introduced to get more accurate prediction results. Compared with other network traffic prediction models, NTAM-LSTM prediction model can achieve higher prediction accuracy and take shorter running time.
关键词：Network traffic prediction;LSTM neural network;Attention mechanism