Publications in Scientific Journals:
M. Laner, P. Svoboda, M. Rupp:
"Parsimonious Fitting of Long-Range Dependent Network Traffic Using ARMA Models";
IEEE Communications Letters,
17
(2013),
12;
2368
- 2371.
English abstract:
ARMA models are well-suited for capturing auto-
correlations of time series. However, in the context of network
traffic modeling they are rarely used for their often claimed
inappropriateness for fitting Long Range Dependence (LRD)
processes. This letter provides evidence that LRD effects can
be well approximated by ARMA models; but only the classical
fitting algorithms are inappropriate for this task. Accordingly,
we propose a novel algorithm, which deploys a multi-scale fitting
procedure. It achieves high accuracy up to an arbitrary cut-
off lag, yielding parsimonious ARMA models. Our findings
encourage a stronger integration of the ARMA framework into
the field of network traffic modeling.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/LCOMM.2013.102613.131853
Created from the Publication Database of the Vienna University of Technology.