Accession Number:

ADA109479

Title:

On Segmentation of Time Series.

Descriptive Note:

Technical rept.,

Corporate Author:

ILLINOIS UNIV AT CHICAGO CIRCLE DEPT OF MATHEMATICS

Personal Author(s):

Report Date:

1981-11-30

Pagination or Media Count:

21.0

Abstract:

The problem of partitioning a time-series into segments is considered. The segments fall into classes, which may correspond to phases of a cycle recession, recovery, expansion in the business cycle or to portions of a signal obtained by scanning backgroundclutter, target, backgroundclutter again, another target, etc. or normal tissue, tumor, normal tissue. Parametric families of distributions are considered, a set of parameter values being associated with each class. With each observation is associated an unobservable label, indicating from which class the observation arose. The label process is modeled as a Markov chain. Segmentation algorithms are obtained by applying a method of iterated maximum likelihood to the resulting likelihood function. In this paper special attention is given to the situation in which the observations are conditionally independent, given the labels. A numerical example is given. Choice of the number of classes, using Akaikes automatic model identification criterion AIC, is illustrated. Prediction is considered. Author

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE