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International Teletraffic Congress

ITC 2007: Managing Traffic Performance in Converged Networks pp 260–271Cite as

State-Space Modeling of Long-Range Dependent Teletraffic

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Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 4516))

Abstract

This paper develops a new state-space model for long-range dependent (LRD) teletraffic. A key advantage of the state-space approach is that forecasts can be performed on-line via the Kalman predictor. The new model is a finite-dimensional (i. e., truncated) state-space representation of the FARIMA (fractional autoregressive integrated moving average) process. Furthermore, we investigate, via simulations, the multistep ahead forecasts obtained from the new model and compare them with those achieved by fitting high-order autoregressive (AR) models.

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Authors and Affiliations

  1. Communications and Signals Laboratory, Department of Telecommunications and Control Engineering, School of Engineering, University of São Paulo,  

    Alexandre B. de Lima & José R. de A. Amazonas

Authors
  1. Alexandre B. de Lima
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  2. José R. de A. Amazonas
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Editor information

Lorne Mason Tadeusz Drwiega James Yan

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© 2007 Springer-Verlag Berlin Heidelberg

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de Lima, A.B., de A. Amazonas, J.R. (2007). State-Space Modeling of Long-Range Dependent Teletraffic. In: Mason, L., Drwiega, T., Yan, J. (eds) Managing Traffic Performance in Converged Networks. ITC 2007. Lecture Notes in Computer Science, vol 4516. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72990-7_26

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  • DOI: https://doi.org/10.1007/978-3-540-72990-7_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72989-1

  • Online ISBN: 978-3-540-72990-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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