Detection of Multiple Changes in Fractional Integrated ARMA Processes

This paper addresses the problem of changepoint detection in FARIMA processes. The received signal is modeled as a FARIMA process, with abrupt changes in the Hurst and ARMA parameters. The proposed changepoint detection method first estimates the model parameters over small segments. The changes are then detected in the parameter vector sequence by minimizing an appropriate least-squares criterion. The cases of known, as well as unknown, number of changes are investigated. Dynamic programming is used to solve this optimization problem. A theoretical analysis of the statistical properties of the change point estimates is provided. Simulation results on synthetic data and real network traffic data are presented.