Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems

Michael McCullough, Michael Small, Thomas Stemler, Herbert Ho Ching Iu

Research output: Contribution to journalArticlepeer-review

120 Citations (Scopus)

Abstract

We investigate a generalised version of the recently proposed ordinal partition time series to network transformation algorithm. First, we introduce a fixed time lag for the elements of each partition that is selected using techniques from traditional time delay embedding. The resulting partitions define regions in the embedding phase space that are mapped to nodes in the network space. Edges are allocated between nodes based on temporal succession thus creating a Markov chain representation of the time series. We then apply this new transformation algorithm to time series generated by the Rössler system and find that periodic dynamics translate to ring structures whereas chaotic time series translate to band or tube-like structures-thereby indicating that our algorithm generates networks whose structure is sensitive to system dynamics. Furthermore, we demonstrate that simple network measures including the mean out degree and variance of out degrees can track changes in the dynamical behaviour in a manner comparable to the largest Lyapunov exponent. We also apply the same analysis to experimental time series generated by a diode resonator circuit and show that the network size, mean shortest path length, and network diameter are highly sensitive to the interior crisis captured in this particular data set.

Original languageEnglish
Article number053101
JournalChaos
Volume25
Issue number5
DOIs
Publication statusPublished - 4 May 2015
Externally publishedYes

Fingerprint

Dive into the research topics of 'Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems'. Together they form a unique fingerprint.

Cite this