Jump detection and fast parameter tracking for piecewise AR processes using adaptive lattice filters

We define a piecewise AR model For a class of time series whose statistical properties change abruptly at some unknown time points. For such a model we consider the problems of jump detection and fast tracking of the changing parameters. A method based on the adaptive least squares lattice filter algorithm is proposed. The method automatically detects the occurrences of jumps and adjusts the adaptation rate of the adaptive lattice algorithm, so both accurate estimates and fast tracking ability are achieved.