scholarly journals NTAM-LSTM models of network traffic prediction

2022 ◽  
Vol 355 ◽  
pp. 02007
Author(s):  
Jihong Zhao ◽  
Xiaoyuan He
Keyword(s):  
Prediction Model ◽  
Network Traffic ◽  
Prediction Models ◽  
Rapid Development ◽  
Attention Mechanism ◽  
Accurate Prediction ◽  
Traffic Prediction ◽  
Network Resources ◽  
Multiple Characteristics ◽  
Lstm Network

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.

The Prediction Model Research for Network Traffic Based on ARMA

2014 ◽  
Vol 530-531 ◽  
pp. 760-763
Author(s):  
Zhao Ji Zhang
Keyword(s):  
Prediction Model ◽  
Network Traffic ◽  
Detection System ◽  
Moving Average ◽  
Third Party ◽  
Testing System ◽  
Network Resources ◽  
Network Intrusion ◽  
Model Research ◽  
Auto Regressive

This paper presents a new WIA-PA network intrusion detection system -- Auto Regressive and Moving Average (ARMA) network traffic prediction model. This model can predict the network traffic quickly and accurately, and because this is a third party testing system, it does not need to take network resources, the security of the WIA-PA network design is of vital importance. The simulation results show that our proposed system can effectively detect intrusion attack, improve the performance of the entire network, prolonging the life of the network.

scholarly journals Variation-Oriented Data Filtering for Improvement in Model Complexity of Air Pollutant Prediction Model

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Chi Man Vong ◽  
Weng Fai Ip ◽  
Pak Kin Wong
Keyword(s):  
Prediction Model ◽  
Air Pollutants ◽  
Prediction Models ◽  
Air Pollutant ◽  
Accurate Prediction ◽  
Model Complexity ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Training Time ◽  
Model Size

Accurate prediction models for air pollutants are crucial for forecast and health alarm to local inhabitants. In recent literature,discrete wavelet transform(DWT) was employed to decompose a series of air pollutant levels, followed by modeling usingsupport vector machine(SVM). This combination of DWT and SVM was reported to produce a more accurate prediction model for air pollutants by investigating different levels of frequency bands. However, DWT has a significant demand in model complexity, namely, the training time and the model size of the prediction model. In this paper, a new method calledvariation-oriented filtering(VF) is proposed to remove the data with low variation, which can be considered asnoiseto a prediction model. By VF, the noise and the size of the series of air pollutant levels can be reduced simultaneously and hence so are the training time and model size. The SO2(sulfur dioxide) level in Macau was selected as a test case. Experimental results show that VF can effectively and efficiently reduce the model complexity with improvement in predictive accuracy.

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