From time series to complex networks: The visibility graph

@article{citeulike:2621415, 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. 10.1073/pnas.0709247105}, author = {Lacasa, Lucas and Luque, Bartolo and Ballesteros, Fernando and Luque, Jordi and Nuno, Juan C. }, citeulike-article-id = {2621415}, doi = {http://dx.doi.org/10.1073/pnas.0709247105}, journal = {Proceedings of the National Academy of Sciences}, keywords = {analysis}, month = {April}, number = {13}, pages = {4972--4975}, posted-at = {2008-04-26 22:43:14}, priority = {0}, title = {From time series to complex networks: The visibility graph}, url = {http://dx.doi.org/10.1073/pnas.0709247105}, volume = {105}, year = {2008} }

TY - JOUR ID - citeulike:2621415 L3 - citeulike-article-id:2621415 TI - From time series to complex networks: The visibility graph JF - Proceedings of the National Academy of Sciences VL - 105 IS - 13 SP - 4972 EP - 4975 N2 - 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. 10.1073/pnas.0709247105 KW - analysis AU - Lacasa, Lucas AU - Luque, Bartolo AU - Ballesteros, Fernando AU - Luque, Jordi AU - Nuno, Juan PY - 2008/04/01/ UR - http://dx.doi.org/10.1073/pnas.0709247105 ER -