Hurst parameter estimation by wavelet transformation and a filter bank for self-similar traffic

Grabs, Elans; Pētersons, Ernests

doi:10.3103/s0146411615050041

Search citation statements

Order By: Relevance

Paper Sections

Select...

Discussion1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the lower values of the Hurst parameter, values tend to be overstated, and for the higher values understated. A similar behavior was observed by Grab and Petersons [ 43 ], who examined the utilization of the wavelet transform for the Hurst parameter estimation.…”

Section: Discussionsupporting

confidence: 79%

Long-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis

Filus

Domański

Domańska

et al. 2020

Entropy

View full text Add to dashboard Cite

The paper examines the ability of neural networks to classify Internet traffic data in terms of self-similarity expressed by the Hurst exponent. Fractional Gaussian noise is used for the generation of synthetic data for modeling the genuine ones. It is presented that the trained model is capable of classifying the synthetic data obtained from the Pareto distribution and the real traffic data. We present the results of training for different optimizers of the cost function and a different number of convolutional layers in the neural network.

show abstract

Section: Discussionsupporting

confidence: 79%