Analysis of nonstationary time series by mixtures of self-organizing predictors

Jens Kohlmorgen, Steven Lemm, Gunnar Raetsch, Klaus Robert Mueller

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

We present a method for the analysis of time series from drifting or switching dynamics. In extension to existing approaches that identify switches or drifts between stationary dynamical modes, the method allows to analyze even continuously varying dynamics and can identify mixtures of more than two dynamical modes. The architecture is based on a mixture of self-organizing Nadaraya-Watson kernel estimators. The mixture model is trained by barrier optimization, a technique for constrained optimization problems. We apply the proposed method to artificially generated data and EEG recordings from the wake/sleep transition.

Original languageEnglish
Pages85-94
Number of pages10
Publication statusPublished - 2000
Event10th IEEE Workshop on Neural Netwoks for Signal Processing (NNSP2000) - Sydney, Australia
Duration: 2000 Dec 112000 Dec 13

Other

Other10th IEEE Workshop on Neural Netwoks for Signal Processing (NNSP2000)
CitySydney, Australia
Period00/12/1100/12/13

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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  • Cite this

    Kohlmorgen, J., Lemm, S., Raetsch, G., & Mueller, K. R. (2000). Analysis of nonstationary time series by mixtures of self-organizing predictors. 85-94. Paper presented at 10th IEEE Workshop on Neural Netwoks for Signal Processing (NNSP2000), Sydney, Australia, .