Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-27T11:03:18.353Z Has data issue: false hasContentIssue false
  • English
  • Français

NONLINEAR TIME SERIES ANALYSIS USING ORDINAL NETWORKS WITH SELECT APPLICATIONS IN BIOMEDICAL SIGNAL PROCESSING

Part of: Approximation methods and numerical treatment of dynamical systems

Published online by Cambridge University Press:  17 May 2019

MICHAEL HUGH MCCULLOUGH Open the ORCID record for MICHAEL HUGH MCCULLOUGH [Opens in a new window]
MICHAEL HUGH MCCULLOUGH*
Affiliation:
Queensland Brain Institute, University of Queensland, St Lucia 4072, Queensland, Australia email m.mccullough@uq.edu.au
Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Keywords

nonlinear time series analysisordinal patternsentropycomplex networkssymbolic dynamics

MSC classification

Primary: 37M10: Time series analysis
Secondary: 37M25: Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy)
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 100 , Issue 1 , August 2019 , pp. 170 - 172
Copyright
© 2019 Australian Mathematical Publishing Association Inc. 

Footnotes

Thesis submitted to The University of Western Australia in October 2017; degree approved 29 January 2018; coordinating supervisor Michael Small, supervisors Thomas Stemler and Herbert Iu.

References

McCullough, M., Sakellariou, K., Stemler, T. and Small, M., ‘Counting forbidden patterns in irregularly sampled time series. I. The effects of under-sampling, random depletion, and timing jitter’, Chaos 26(12) (2016), Article ID 123103.Google Scholar
McCullough, M., Sakellariou, K., Stemler, T. and Small, M., ‘Regenerating time series from ordinal networks’, Chaos 27(3) (2017), Article ID 035814.Google Scholar
McCullough, M., Small, M., Iu, H. and Stemler, T., ‘Multiscale ordinal network analysis of human cardiac dynamics’, Philos. Trans. R. Soc. Lond. Ser. A 375(2096) (2017), Article ID 20160292, 17 pages.Google Scholar
McCullough, M., Small, M., Stemler, T. and Iu, H. H.-C., ‘Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems’, Chaos 25(5) (2015), Article ID 053101.Google Scholar
Small, M., McCullough, M. and Sakellariou, K., ‘Ordinal network measures—quantifying determinism in data’, in: 2018 IEEE International Symposium on Circuits and Systems (ISCAS) (IEEE, New York, 2018), 1–5.Google Scholar