Analysis of drifting dynamics with competing predictors

J. Kohlmorgen, Klaus Muller, K. Pawelzik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A method for the analysis of nonstationary time series with multiple modes of behaviour is presented. In particular, it is not only possible to detect a switching of dynamics but also a less abrupt, time consuming drift from one mode to another. This is achieved by an unsupervised algorithm for segmenting the data according to the modes and a subsequent search through the space of possible drifts. Applications to speech and physiological data demonstrate that analysis and modeling of real world time series can be improved when the drift paradigm is taken into account.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages785-790
Number of pages6
Volume1112 LNCS
ISBN (Print)3540615105, 9783540615101
DOIs
Publication statusPublished - 1996 Jan 1
Externally publishedYes
Event1996 International Conference on Artificial Neural Networks, ICANN 1996 - Bochum, Germany
Duration: 1996 Jul 161996 Jul 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1112 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1996 International Conference on Artificial Neural Networks, ICANN 1996
CountryGermany
CityBochum
Period96/7/1696/7/19

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Kohlmorgen, J., Muller, K., & Pawelzik, K. (1996). Analysis of drifting dynamics with competing predictors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 785-790). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1112 LNCS). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_132