Geometric description and characterization of time series signals

Lauren N. Crider, Douglas Cochran

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

Abstract

This paper considers time series signals in Rn as samples of an embedded space curve and proceeds to characterize such signals in terms of differential-geometric descriptors of their associated curves. In particular, a method of estimating curvature as a function of arc length is presented. Because arc length is invariant to reparameterization of a space curve, this approach provides a representation of the evolution of the time series that is invariant to local variations in the rate of the time series as well as displacement and rotation of the curve in space. The focus here is on ascertaining structural similarity of time series signals by measuring similarity of their curvatures, though extension to other applications and other geometric descriptors (e.g., torsion) is envisioned.

Original languageEnglish (US)
Title of host publicationConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages382-386
Number of pages5
Volume2017-October
ISBN (Electronic)9781538618233
DOIs
StatePublished - Apr 10 2018
Event51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States
Duration: Oct 29 2017Nov 1 2017

Other

Other51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
CountryUnited States
CityPacific Grove
Period10/29/1711/1/17

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ASJC Scopus subject areas

  • Control and Optimization
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Biomedical Engineering
  • Instrumentation

Cite this

Crider, L. N., & Cochran, D. (2018). Geometric description and characterization of time series signals. In M. B. Matthews (Ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 (Vol. 2017-October, pp. 382-386). [8335364] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACSSC.2017.8335364