Comparative evaluation of ARIMA and ANFIS for modeling of wireless 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.

Download Full-text

A Time Series Modeling and Prediction of Wireless Network Traffic

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v3i1.284 ◽

2009 ◽

Vol 3 (1) ◽

Cited By ~ 6

Author(s):

S. Gowrishankar ◽

P. S. Satyanarayana

Keyword(s):

Time Series ◽

Wireless Network ◽

Network Traffic ◽

Time Series Modeling ◽

Modeling And Prediction

Download Full-text

Time Series Prediction of Wireless Network Traffic Flow Based on Wavelet Analysis and BP Neural Network

Journal of Physics Conference Series ◽

10.1088/1742-6596/1533/3/032098 ◽

2020 ◽

Vol 1533 ◽

pp. 032098 ◽

Cited By ~ 1

Author(s):

Yanchi Li ◽

Jin Huang ◽

Haojie Chen

Keyword(s):

Neural Network ◽

Time Series ◽

Wavelet Analysis ◽

Traffic Flow ◽

Wireless Network ◽

Bp Neural Network ◽

Network Traffic ◽

Time Series Prediction

Download Full-text

Chaotic characteristic analysis of network traffic time series at different time scales

Chaos Solitons & Fractals ◽

10.1016/j.chaos.2019.109412 ◽

2020 ◽

Vol 130 ◽

pp. 109412 ◽

Cited By ~ 7

Author(s):

Zhongda Tian

Keyword(s):

Time Series ◽

Time Scales ◽

Network Traffic ◽

Characteristic Analysis ◽

Traffic Time Series ◽

Chaotic Characteristic ◽

Different Time Scales

Download Full-text

Wavelet-analysis of network traffic time-series for detection of attacks on digital production infrastructure

SHS Web of Conferences ◽

10.1051/shsconf/20184400052 ◽

2018 ◽

Vol 44 ◽

pp. 00052 ◽

Cited By ~ 3

Author(s):

Darya Lavrova ◽

Pavel Semyanov ◽

Anna Shtyrkina ◽

Peter Zegzhda

Keyword(s):

Time Series ◽

Network Traffic ◽

Change Point ◽

Posteriori Probability ◽

Network Attacks ◽

A Posteriori Probability ◽

Traffic Time Series ◽

Digital Production ◽

Points Of View ◽

Digital Manufacture

Digital production integrates with all the areas of human activity including critical industries, therefore the task of detecting network attacks has a key priority in protecting digital manufacture systems. This article offers an approach for analysis of digital production security based on evaluation of a posteriori probability for change point in time-series, which are based on the change point coefficient values of digital wavelet-transform in the network traffic time-series. These time-series make it possible to consider the network traffic from several points of view at the same time, which plays an important role in the task of detecting network attacks. The attack methods vary significantly; therefore, in order to detect them it is necessary to monitor different values of various traffic parameters. The proposed method has demonstrated its efficiency in detecting network service denial attacks (SlowLoris and HTTP DoS) being realized at the application level.

Download Full-text