On Segmentation of Time Series.

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 (background/clutter, target, background/clutter 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 Akaike's automatic (model) identification criterion (AIC), is illustrated. Prediction is considered. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Nov 30, 1981
Accession Number
ADA109479

Entities

People

  • Stanley L. Sclove

Organizations

  • University of Illinois at Chicago

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Automatic
  • Computer Vision
  • Data Science
  • Data Sets
  • Identification
  • Illinois
  • Information Science
  • Markov Chains
  • Mathematics
  • Military Research
  • Probability
  • Probability Density Functions
  • Random Variables
  • Standards
  • Stochastic Processes
  • United States

Fields of Study

  • Mathematics

Readers

  • Astronomy/Astrophysics
  • Computer Vision.
  • Regression Analysis.