Analysis of drifting dynamics with competing predictors

J. Kohlmorgen, K. R. Müller, K. Pawelzik

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


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 publicationArtificial Neural Networks, ICANN 1996 - 1996 International Conference, Proceedings
PublisherSpringer Verlag
Number of pages6
ISBN (Print)3540615105, 9783540615101
Publication statusPublished - 1996
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)0302-9743
ISSN (Electronic)1611-3349


Conference1996 International Conference on Artificial Neural Networks, ICANN 1996

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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