Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/112635
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Type: Conference paper
Title: A comparison of information criteria for traffic model selection
Author: Tune, P.
Roughan, M.
Cho, K.
Citation: Proceedings of the 10th International Conference on Signal Processing and Communication Systems (ICSPCS 2016), 2016 / Wysocki, T., Wysocki, B. (ed./s), pp.1-10
Publisher: IEEE
Publisher Place: Piscataway, NJ
Issue Date: 2016
ISBN: 9781509009428
Conference Name: 10th International Conference on Signal Processing and Communication Systems (ICSPCS 2016) (19 Dec 2016 - 21 Dec 2016 : Surfers Paradise)
Statement of
Responsibility: 
Paul Tune, Matthew Roughan, Kenjiro Cho
Abstract: Traffic modelling is a core component of network planning and engineering. Although good models are approximations of reality, they are very useful in various network applications. However, traffic modelling is often done in an ad hoc manner, guided only by the experience of the model designer. In this paper, we propose the use of information criteria, such as the Akaike Information Criterion (AIC), to systematically choose models. We study these criteria on Frequency, Frequency + Spike, and Wavelet models of the network traffic to select the best of these. However, there are many alternative information criteria, which give different results. We found that the Bayesian Information Criterion (BIC), and Minimum Description Length (MDL) provided better models than the (perhaps) more commonly used AIC and corrected AIC for network traffic modelling. Interestingly, we found that fancier models, such as Wavelet models, may reduce prediction accuracy, so simple frequencybased models are preferable.
Rights: ©2016 IEEE
RMID: 0030083934
DOI: 10.1109/ICSPCS.2016.7843299
Published version: https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7829189
Appears in Collections:Mathematical Sciences publications

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