Time-Series Segmentation: A Model and a Method.

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.

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Document Details

Document Type
Technical Report
Publication Date
Dec 22, 1982
Accession Number
ADA123206

Entities

People

  • Stanley L. Sclove

Organizations

  • University of Illinois at Chicago

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Communities of Interest

  • Cyber
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Business Administration
  • Computer Programs
  • Illinois
  • Image Processing
  • Information Processing
  • Information Science
  • Markov Chains
  • Markov Processes
  • Military Research
  • Pattern Recognition
  • Probability
  • Probability Distributions
  • Random Variables
  • Stochastic Processes
  • United States
  • Universities

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  • Computer Vision.
  • Statistical inference.