Parameter estimation of locally stationary wavelet processes

This paper considers the extraction of information from a locally stationary process modelled by wavelet packets. A method is presented to select subprocesses that characterize the key aspects of the nonstationary process for pattern analysis. The estimated parameters of the selected subprocesses are used to infer the process' time varying behavior. The estimated parameters can be used as features in the attempt to distinguish changing states within a process or differentiate two different locally stationary processes.