State-Space Modeling of Long-Range Dependent Teletraffic

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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