State-space modeling of long-range dependent teletraffic

Authors:
Alexandre B. De Lima;José R. De A. Amazonas
Affiliations:
Communications and Signals Laboratory, Department of Telecommunications and Control Engineering, School of Engineering, University of São Paulo;Communications and Signals Laboratory, Department of Telecommunications and Control Engineering, School of Engineering, University of São Paulo
Venue:
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
Year:
2007

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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.