A model for non-stationary time series analysis with clustering methods

Shai Policker, Amir B. Geva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The object of this paper is to present a model of non-stationary time series generated by switching between a finite number of random processes and to apply clustering algorithms to the task of estimating the model’s parameters. We will also analyze the parameters which govern the algorithm’s behavior to infer a novel cluster validity criterion for fuzzy clustering algorithms of temporal patterns. The model defines a non-stationary composite source generated by randomly switching between elements of a finite number of random processes. The probability distribution which underlies the behavior of the switch is controlled by a temporal parameter vector process which is used to determine a different switching probability in each time instant. This definition allows us to analyze a drift between disjoint states of the composite model.

Original languageEnglish
Title of host publicationAdvances in Pattern Recognition - Joint IAPR International Workshops SSPR 1998 and SPR 1998, Proceedings
EditorsAdnan Amin, Dov Dori, Pavel Pudil, Herbert Freeman
PublisherSpringer Verlag
Pages649-657
Number of pages9
ISBN (Print)3540648585, 9783540648581
DOIs
StatePublished - 1 Jan 1998
Event7th Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in Pattern Recognition, SPR 1998 - Sydney, Australia
Duration: 11 Aug 199813 Aug 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1451
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in Pattern Recognition, SPR 1998
Country/TerritoryAustralia
CitySydney
Period11/08/9813/08/98

Keywords

  • Fuzzy clustering
  • Temporal pattern recognition
  • Time series analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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