Network Traffic Prediction Method Based on Time Series Characteristics

Huo, Yonghua; Song, Chunxiao; Gao, Sheng; Yang, Haodong; Yan, Yu; Yang, Yang

doi:10.1007/978-981-15-8462-6_175

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2022

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“…By optimizing the allocation of network resources, the network user's experience can be improved. Therefore, the prediction of network traffic [2] plays a very important role in the development planning of modern network.…”

Section: Introductionmentioning

confidence: 99%

NTAM-LSTM models of network traffic prediction

Zhao

2022

MATEC Web Conf.

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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.

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Section: Introductionmentioning

confidence: 99%

NTAM-LSTM models of network traffic prediction

Zhao

2022

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

Network Traffic Prediction Method Based on Time Series Characteristics

Cited by 1 publication

References 4 publications

NTAM-LSTM models of network traffic prediction

NTAM-LSTM models of network traffic prediction

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