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A class of models for aggregated traffic volume time series
Author(s) -
Brockwell A. E.,
Chan N. H.,
Lee P. K.
Publication year - 2003
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00414
Subject(s) - volume (thermodynamics) , series (stratigraphy) , computer science , workstation , time series , traffic volume , internet traffic , data mining , gaussian , class (philosophy) , algorithm , real time computing , the internet , artificial intelligence , machine learning , engineering , transport engineering , paleontology , physics , quantum mechanics , world wide web , operating system , biology
Summary. The development of time series models for traffic volume data constitutes an important step in constructing automated tools for the management of computing infrastructure resources. We analyse two traffic volume time series: one is the volume of hard disc activity, aggregated into half‐hour periods, measured on a workstation, and the other is the volume of Internet requests made to a workstation. Both of these time series exhibit features that are typical of network traffic data, namely strong seasonal components and highly non‐Gaussian distributions. For these time series, a particular class of non‐linear state space models is proposed, and practical techniques for model fitting and forecasting are demonstrated.