On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Experimental queueing analysis with long-range dependent packet traffic
IEEE/ACM Transactions on Networking (TON)
A new heavy-tailed discrete distribution for LRD M/G/∞ sample generation
Performance Evaluation
Queueing at large resources driven by long-tailed M/G/\infty-modulated processes
Queueing Systems: Theory and Applications
Fast simulation of self-similar and correlated processes
Mathematics and Computers in Simulation
Modeling video traffic using M/G/∞ input processes: a compromise between Markovian and LRD models
IEEE Journal on Selected Areas in Communications
MPEG-4 and H.263 video traces for network performance evaluation
IEEE Network: The Magazine of Global Internetworking
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In this paper, we study a novel empirical criterion for identifying the type of a long-memory time series. The proposed rule for selecting an underlying model suitable to the data is based on the comparison between the normalized prediction errors of the generalized Whittle estimator (a parametric spectral estimator) applied to two or more candidate models. We test this approach by two applications of the procedure: for comparing two distinct statistical models to adjust the data, and for assessing the significance of increasing the number of parameters within a given class of models. Due to the heuristic nature of the method, we test the statistic numerically for several classes of stochastic processes, namely Gaussian processes with long-range dependence (LRD), M/G/~ (non-Gaussian) processes with LRD, non-stationary processes, and non-linear heteroscedastic models. The numerical results demonstrate that the proposed statistic exhibits good power, is robust and not computationally expensive.