In this work we present a simple and fast computational method, the
visibility algorithm, that converts a time series into a graph. The
constructed graph inherits several properties of the series in its
structure. Thereby, periodic series convert into regular graphs, and
random series do so into random graphs. Moreover, fractal series
convert into scale-free networks, enhancing the fact that power law
degree distributions are related to fractality, something highly
discussed recently. Some remarkable examples and analytical tools are
outlined to test the method’s reliability. Many different measures,
recently developed in the complex network theory, could by means of
this new approach characterize time series from a new point of view.
%0 Journal Article
%1 Lacasa_2008
%A Lacasa, Lucas
%A Luque, Bartolo
%A Ballesteros, Fernando
%A Luque, Jordi
%A Nuno, Juan Carlos
%D 2008
%K 1/f_noise, complex_networkx, networkx, time_series, universality
%N 13
%T From time series to complex networks: The visibility graph
%V 105
%X In this work we present a simple and fast computational method, the
visibility algorithm, that converts a time series into a graph. The
constructed graph inherits several properties of the series in its
structure. Thereby, periodic series convert into regular graphs, and
random series do so into random graphs. Moreover, fractal series
convert into scale-free networks, enhancing the fact that power law
degree distributions are related to fractality, something highly
discussed recently. Some remarkable examples and analytical tools are
outlined to test the method’s reliability. Many different measures,
recently developed in the complex network theory, could by means of
this new approach characterize time series from a new point of view.
@article{Lacasa_2008,
abstract = {In this work we present a simple and fast computational method, the
visibility algorithm, that converts a time series into a graph. The
constructed graph inherits several properties of the series in its
structure. Thereby, periodic series convert into regular graphs, and
random series do so into random graphs. Moreover, fractal series
convert into scale-free networks, enhancing the fact that power law
degree distributions are related to fractality, something highly
discussed recently. Some remarkable examples and analytical tools are
outlined to test the method’s reliability. Many different measures,
recently developed in the complex network theory, could by means of
this new approach characterize time series from a new point of view.},
added-at = {2010-05-10T08:12:01.000+0200},
author = {Lacasa, Lucas and Luque, Bartolo and Ballesteros, Fernando and Luque, Jordi and Nuno, Juan Carlos},
biburl = {https://www.bibsonomy.org/bibtex/2bd72d5f761403e82ae2da49939ea1d65/dhruvbansal},
file = {/home/dhruv/projects/work/papers/papers/Lacasa_2008.pdf},
interhash = {67ec6e442afa5d138f4b1a9e9a1140a7},
intrahash = {bd72d5f761403e82ae2da49939ea1d65},
keywords = {1/f_noise, complex_networkx, networkx, time_series, universality},
month = {April},
number = 13,
read = {nil},
timestamp = {2010-05-10T08:12:04.000+0200},
title = {From time series to complex networks: The visibility graph},
volume = 105,
year = 2008
}