Analyzing and Visualizing Anomalies and Events in Time Series of Network Traffic

2019 ◽  
pp. 15-25
Author(s):  
Qinpei Zhao ◽  
Yinjia Zhang ◽  
Yang Shi ◽  
Jiangfeng Li
Keyword(s):  
Time Series ◽  
Network Traffic

Construction of C-ON/OFF network traffic model based on time series

2018 ◽  
Vol 35 (3) ◽  
pp. 2867-2877
Author(s):  
Yi Liu ◽  
Tian Song ◽  
Le-Jian Liao
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Traffic Model ◽  
Model Based ◽  
Network Traffic Model

A Traffic Prediction Method Based on the Hybrid Model of CLWT

2015 ◽  
Vol 713-715 ◽  
pp. 1564-1569
Author(s):  
Jin Long Fei ◽  
Wei Lin ◽  
Tao Han ◽  
Yue Fei Zhu
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Network Traffic ◽  
Prediction Models ◽  
Prediction Method ◽  
Support Vector ◽  
Prediction Methods ◽  
Recurrent Nerve ◽  
Nerve Network ◽  
Traffic Time Series

Current prediction models for network traffic cannot accurately depict the multi-properties of the Internet traffic. This paper proposes a wavelet-based hybrid model prediction method for network traffic called CLWT model and proposes a prediction method for traffic based on this model. The traffic time series can be rapidly decomposed respectively into approximate time series and detail time series with LF and HF response. The approximate time series predicts by making use of Least Squares Support Vector Machine and proceeds error calibration by using Generalized Recurrent Nerve Network. The detail time series predict it by making use of self-adaption chaotic prediction methods after the medium-soft threshold noise reduction. Finally the prediction value of time series is got by making use of promoting wavelet reconstitution. The effectiveness for the prediction methods mentioned in the paper has been validated by simulation experiment. High prediction accuracy is obtained compared with the existing methods.

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