Analysis of drifting dynamics with neural network hidden markov models

J. Kohlmorgen, K. R. Müllerc, K. Pawelzik

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

3 Citations (Scopus)

Abstract

We present a method for the analysis of nonstationary time series with multiple operating modes. In particular, it is possible to detect and to model both a switching of the dynamics and a less abrupt, time consuming drift from one mode to another. This is achieved in two steps. First, an unsupervised training method provides prediction experts for the inherent dynamical modes. Then, the trained experts are used in a hidden Markov model that allows to model drifts. An application to physiological wake/sleep data demonstrates 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 publicationAdvances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997
PublisherNeural information processing systems foundation
Pages735-741
Number of pages7
ISBN (Print)0262100762, 9780262100762
Publication statusPublished - 1998
Event11th Annual Conference on Neural Information Processing Systems, NIPS 1997 - Denver, CO, United States
Duration: 1997 Dec 11997 Dec 6

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other11th Annual Conference on Neural Information Processing Systems, NIPS 1997
CountryUnited States
CityDenver, CO
Period97/12/197/12/6

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Kohlmorgen, J., Müllerc, K. R., & Pawelzik, K. (1998). Analysis of drifting dynamics with neural network hidden markov models. In Advances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997 (pp. 735-741). (Advances in Neural Information Processing Systems). Neural information processing systems foundation.