Ordinal symbolic network methodologies for nonlinear time series analysis

Konstantinos Sakellariou

Research output: ThesisDoctoral Thesis

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Analysing nonlinear interactions and chaotic dynamics is essential for the study of complex systems. This thesis investigates
network-based techniques founded on the symbolic dynamics of ordinal patterns, i.s. a specific encoding of order relations
between successive measurements In a time series. Findings show that our proposed Mancovlan framework based on an
ordinal partition of state space can extract meaningful information purely from scalar projections of mixing multidimensional systems. By employing ergodic-theoretic tools, we show that such stochastic approximations to deterministic dynamics yield accurate estimates for topological and metric dynamical invariants in both discrete- and continuous-time systems.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
  • Small, Michael, Supervisor
  • Judd, Kevin Thomas, Supervisor
Award date4 Jan 2018
Publication statusUnpublished - 2018


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