Least squares detection of multiple changes in fractional ARIMA processes

We address the problem of estimating changes in fractional integrated ARMA (FARIMA) processes. These changes may be in the Long Range Dependence (LRD) parameter or the ARMA parameters. The signal is divided into "elementary" segments: the objective is then to estimate the segments in which the changes occur. This estimation is achieved by minimizing a penalized least-squares criterion based on the parameter estimates computed in each segment. The optimization problem is then solved using a dynamic programming algorithm. Simulation results on synthetic data are reported.