2008 Congress on Image and Signal Processing 2008
DOI: 10.1109/cisp.2008.719
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A Time-Varying FARIMA Model for Internet Traffic

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Cited by 6 publications
(4 citation statements)
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“…Among the processes possessing such property are FARIMA discrete processes and multifractals. Let us recall that FARIMA models have been developed to generate time series with long-range dependence [48,49] and have been applied to model experimental data in fields such as hydrology [50,51], ocean science [52], meteorology [53], solar physics [54] or traffic network [55,56]; see also [57,58] for numerical works. We can thus see that multifractals and FARIMA models are two amongst the realistic and popular alternative models applied for understanding and assessing the long-range properties of time series.…”
Section: Discussionmentioning
confidence: 99%
“…Among the processes possessing such property are FARIMA discrete processes and multifractals. Let us recall that FARIMA models have been developed to generate time series with long-range dependence [48,49] and have been applied to model experimental data in fields such as hydrology [50,51], ocean science [52], meteorology [53], solar physics [54] or traffic network [55,56]; see also [57,58] for numerical works. We can thus see that multifractals and FARIMA models are two amongst the realistic and popular alternative models applied for understanding and assessing the long-range properties of time series.…”
Section: Discussionmentioning
confidence: 99%
“…In recent years, terrestrial network‐based traffic prediction methods have received extensive attention from researchers. Traditional network traffic prediction methods, such as autoregressive integrated moving average (ARIMA) model, 20 fractional autoregressive integrated moving average (FARIMA) model, 21 and wavelet packet‐autocorrelation Function, 22 have achieved good prediction performance in the field of traffic prediction for terrestrial networks.…”
Section: Related Workmentioning
confidence: 99%
“…However, previous models, including Markov process, Poisson process, autoregressive (AR) model and moving average (ARMA) model and so on, can only describe the data of short-range dependence (SRD) [3]. Yet, self-similar models such as fractional gaussian noise (FGN) model and fractional Brownian motion (FBM) model have been developed to resolve this problem.…”
Section: Introductionmentioning
confidence: 99%
“…Unfortunately, these models are incapable of giving a description of SRD, which is also a significant property of the network traffic. Fractional autoregressive integrated moving average (FARIMA) model is provided by this work to reveal the co-existence of both SRD and LRD in the network flow [3], and the model also has a wide range of applications in other domains. Dr Kristoufek found that the Hurst exponent he got from the time series he previously generated had large deviation to the expected one which was used to generate the time series [1].…”
Section: Introductionmentioning
confidence: 99%