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 problem of change point detection when the properties of the random process observed suddenly begin changing slowly is considered. The most probable time moments of changes are investigated. Random processes are described by autoregression equations. The situation is studied when slow changes in the properties of a random process take place according to the linear law. An example of solving the problem is presented, realized by computer.