Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009, Revised Papers, Part 2

Research Article

Transforming Time Series into Complex Networks

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  • @INPROCEEDINGS{10.1007/978-3-642-02469-6_84,
        author={Michael Small and Jie Zhang and Xiaoke Xu},
        title={Transforming Time Series into Complex Networks},
        proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009, Revised Papers, Part 2},
        proceedings_a={COMPLEX PART 2},
        year={2012},
        month={5},
        keywords={nonlinear time series chaos chaotic dynamics complex networks},
        doi={10.1007/978-3-642-02469-6_84}
    }
    
  • Michael Small
    Jie Zhang
    Xiaoke Xu
    Year: 2012
    Transforming Time Series into Complex Networks
    COMPLEX PART 2
    Springer
    DOI: 10.1007/978-3-642-02469-6_84
Michael Small1,*, Jie Zhang1, Xiaoke Xu1
  • 1: Hong Kong Polytechnic University
*Contact email: ensmall@polyu.edu.hk

Abstract

We introduce transformations from time series data to the domain of complex networks which allow us to characterise the dynamics underlying the time series in terms of topological features of the complex network. We show that specific types of dynamics can be characterised by a specific prevalence in the complex network motifs. For example, low-dimensional chaotic flows with one positive Lyapunov exponent form a single family while noisy non-chaotic dynamics and hyper-chaos are both distinct. We find that the same phenomena is also true for discrete map-like data. These algorithms provide a new way of studying chaotic time series and equip us with a wide range of statistical measures previously not available in the field of nonlinear time series analysis.