Change detection in teletraffic models

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

Change Detection in Teletraffic Models

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

Change Detection in Teletraffic Models

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. Keywords: Change detection, Long memory processes, Autoregressive integrated moving average, Markov modulated Poisson process EDICS: 3-COMM I. Introduction Change detection algorithms have been studied extensively for the past 50 years [1] [2]. Adaptive identification algorithms can track only slow fluctuations of the characteristic parameters and are not suited for detection of..