Accession Number:

ADA123206

Title:

Time-Series Segmentation: A Model and a Method.

Descriptive Note:

Technical rept.,

Corporate Author:

ILLINOIS UNIV AT CHICAGO CIRCLE DEPT OF QUANTITATIVE METHODS

Personal Author(s):

Report Date:

1982-12-22

Pagination or Media Count:

34.0

Abstract:

The problem of partitioning time-series into segments is treated. The segments are considered as falling into classes. A different probability distribution is associated with each class of segment. Parametric families of distribution 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 relaxation method to maximize the resulting likelihood function.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE