Modified embedding for multi-regime detection in nonstationary streaming data

Evan Kriminger, José C. Príncipe, Choudur Lakshminarayan

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

Abstract

Many practical data streams are typically composed of several states known as regimes. In this paper, we invoke phase space reconstruction methods from non-linear time series and dynamical systems for regime detection. But the data collected from sensors is normally noisy, does not have constant amplitude and is sometimes plagued by shifts in the mean. All these aspects make modeling even more difficult. We propose a representation of the time series in the phase space with a modified embedding, which is invariant to translation and scale. The features we use for regime detection are based on comparing trajectory segments in the modified embedding space with cross-correntropy, which is a generalized correlation function. We apply our algorithm to non-linear oscillations, and compare its performance with the standard time delay embedding.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2256-2259
Number of pages4
DOIs
StatePublished - 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • Real time detection
  • correntropy
  • symbolic dynamics
  • time series embedding

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