Change Detection in Teletraffic Models
Date
2000
Authors
Jana, R
Dey, S
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
In this paper, we propose a likelihood-based ratio test to detect distributional changes in common teletraffic models. These include traditional models like the Markov modulated Poisson process and processes exhibiting long range dependency, in particular, Gaussian fractional ARIMA processes. A practical approach is also developed for the case where the parameter after the change is unknown. It is noticed that the algorithm is robust enough to detect slight perturbations of the parameter value after the change. A comprehensive set of numerical results including results for the mean detection delay is provided.
Description
Keywords
Keywords: Algorithms; Markov processes; Mathematical models; Maximum likelihood estimation; Perturbation techniques; Poisson distribution; Signal to noise ratio; Telecommunication traffic; Autoregressive integrated moving average; Change detection; Generalized like
Citation
Collections
Source
IEEE Transactions on Signal Processing
Type
Journal article