Internet Traffic Modeling Using Integer-Valued Time Series

2006 ◽  
Author(s):  
Z. Liu ◽  
J. Almhana ◽  
V. Choulakian ◽  
R. McGorman
Keyword(s):  
Time Series ◽  
Internet Traffic ◽  
Traffic Modeling

An Improved LS-SVR Ensemble Learning in Internet Traffic Prediction

2011 ◽  
Vol 121-126 ◽  
pp. 3794-3798 ◽  
Author(s):  
Kun Lun Li ◽  
Ying Hui Ma ◽  
Yong Mei Tian ◽  
Jing Xie
Keyword(s):  
Time Series ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Internet Traffic ◽  
Traffic Prediction ◽  
Initial Training ◽  
Classification Problems ◽  
Traffic Forecasting ◽  
Ensemble Learning Algorithm

In this paper, we present a new method for internet traffic forecasting based on a boosting LS-SVR algorithm. AdaBoost has been proved to be an effective method for improving the performance of weak learning algorithms and widely applied to classification problems. Inspired by it, we use LS-SVR to complete the initial training; and pay more attention on the “high error areas” in the time series; then, we use an ensemble learning algorithm to learn these areas.

scholarly journals Mining Graph-Fourier Transform Time Series for Anomaly Detection of Internet Traffic at Core and Metro Networks

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Manuel Herrera ◽  
Yaniv Proselkov ◽  
Marco Perez-Hernandez ◽  
Ajith K. Parlikad
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Anomaly Detection ◽  
Internet Traffic ◽  
Metro Networks
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