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    Sequential visibility-graph motifs. 
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    Sequential visibility-graph motifs.

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    ArXiv paper (1015.Kb)
    Volume
    93
    Pagination
    042309 - ?
    Publisher
    American Physical Society
    Issue
    4-1
    ISSN
    1539-3755
    Metadata
    Show full item record
    Abstract
    Visibility algorithms transform time series into graphs and encode dynamical information in their topology, paving the way for graph-theoretical time series analysis as well as building a bridge between nonlinear dynamics and network science. In this work we introduce and study the concept of sequential visibility-graph motifs, smaller substructures of n consecutive nodes that appear with characteristic frequencies. We develop a theory to compute in an exact way the motif profiles associated with general classes of deterministic and stochastic dynamics. We find that this simple property is indeed a highly informative and computationally efficient feature capable of distinguishing among different dynamics and robust against noise contamination. We finally confirm that it can be used in practice to perform unsupervised learning, by extracting motif profiles from experimental heart-rate series and being able, accordingly, to disentangle meditative from other relaxation states. Applications of this general theory include the automatic classification and description of physical, biological, and financial time series.
    Authors
    Iacovacci, J; Lacasa, L
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/15927
    Collections
    • Applied Mathematics [140]
    Language
    ENG
    Copyright statements
    2016 American Physical Society
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