Modelling piecewise long memory signals based on MDL

We consider the problem of modelling piecewise fractional autoregressive integrated moving-average (FARIMA) model signal. The number m of break points as well as their locations, the order (p, q) and the parameters of each regime are assumed to be unknown. To estimate the unknown parameters, we propose a criterion based on the minimum description length (MDL) principle of Rissanen. A genetic algorithm is implemented to optimize this criterion. Monte Carlo simulation results show that criterion performs well for estimating the break points number as well as their locations, the order and the parameters of each regime.