ArticleScientific reports2017
Constructing ordinal partition transition networks from multivariate time series.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Noise Robustness of Transcript-Based Estimators for Properties of Interactions.Entropy (Basel, Switzerland) · 2025Article
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- A new network representation for time series analysis from the perspective of combinatorial property of ordinal patterns.Heliyon · 2023Article
- Hemoglobin signal network mapping reveals novel indicators for precision medicine.Scientific reports · 2023Article
- Unveiling the Connectivity of Complex Networks Using Ordinal Transition Methods.Entropy (Basel, Switzerland) · 2023Article
- Estimating pandemic effects in urban mass transportation systems: An approach based on visibility graphs and network similarity.Physica A · 2023Article
- Topological analysis of differential effects of ketamine and propofol anaesthesia on brain dynamics.Royal Society open science · 2021Article
- Complex networks and deep learning for EEG signal analysis.Cognitive neurodynamics · 2021Review
- Network Analysis of Time Series: Novel Approaches to Network Neuroscience.Frontiers in neuroscience · 2021Review
- Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities.Frontiers in physiology · 2020Article
- Correlations Preceding High-Intensity Events in the Chaotic Dynamics of a Raman Fiber Laser.Entropy (Basel, Switzerland) · 2019Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.