Abstract
This paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on electrostatics in physics. The proposed method conceptualizes a time-series as a series of stationary, electrically charged particles, on which Coulomb-like forces can be computed. This allows generating electrostatic-like graphs associated with time-series that, additionally to the existing transformations, can be also weighted and sometimes disconnected. Within this context, this paper examines the structural similarity between five different types of time-series and their associated graphs that are generated by the proposed algorithm and the visibility graph, which is currently the most popular algorithm in the literature. The analysis compares the source (original) time-series with the node-series generated by network measures (that are arranged into the node-ordering of the source time-series), in terms of a linear trend, chaotic behaviour, stationarity, periodicity, and cyclical structure. It is shown that the proposed electrostatic graph algorithm generates graphs with node-measures that are more representative of the structure of the source time-series than the visibility graph. This makes the proposed algorithm more natural rather than algebraic, in comparison with existing physics-defined methods. The overall approach also suggests a methodological framework for evaluating the structural relevance between the source time-series and their associated graphs produced by any possible transformation.
Highlights
This paper proposes a new method for converting a time-series into a weighted graph, which builds on electrostatics in physics
The visibility graph algorithm (VGA) is by definition restricted in generating visibility graphs that are disassociated from the numerical scale of the source time-series
This approach allows quantifying the interaction between the time-series nodes and to conceptualize the dynamics of a time-series through the effect of electrostatic forces applied between the nodes
Summary
This paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on electrostatics in physics. It is shown that the proposed electrostatic graph algorithm generates graphs with node-measures that are more representative of the structure of the source time-series than the visibility graph This makes the proposed algorithm more natural rather than algebraic, in comparison with existing physics-defined methods. The second one is based on the universality of Coulomb’s inverse square law[17], which grounded the development of essential research in electromagnetism and inspired multidisciplinary research in e conomics[18], urban and spatial planning and transport engineering19,20, biology21, geophysics22, computational[23] and communication sciences[24], etc Within this multidisciplinary context, the proposed method conceptualizes a time-series as a sequence of stationary and electrically charged particles (nodes) and generates an electrostatic graph based on pair-wise calculations of Coulomb’s law across the time-series nodes.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
