A practical method for segmentation and estimation of model parameters of the processes with frequently and instantly changing properties

Article type: Research Article

Authors: Ostaševičius, Egidijus

Affiliations: Institute of Mathematics and Informatics, Lithuanian Academy of Sciences, 2600 Vilnius, Akademijos St.4, Lithuania

Abstract: 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.

Keywords: random process, segmentation, maximum likelihood estimation, pseudo-stationary time series, autoregressive model

DOI: 10.3233/INF-1992-3107

Journal: Informatica, vol. 3, no. 1, pp. 80-87, 1992