Informatica

A practical method for segmentation and estimation of model parameters of processes is proposed in this paper. A pseudo-stationary random process with instantly changing properties is divided into stationary segments. Every segment is described by an autoregressive model. A maximum likehood method is used for segmentation of the random process and estimation of unknown model parameters. An example with simulated data is presented.

The paper defines the decomposition problem of a mixture of time series into homogeneous components. First part deals with a solution based on Bayesian approach in the case of independent observations, the other part is devoted to a solution of on-line decomposition for a time series consisting of weakly stationary components.