Quantifying sudden changes in dynamical systems using symbolic networks
We characterize the evolution of a dynamical system by combining two well-known complex systems' tools, namely, symbolic ordinal analysis and networks. From the ordinal representation of a time series we construct a network in which every node weight represents the probability of an ordinal pat...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/76333 |
| Acceso en línea: | https://hdl.handle.net/2117/76333 https://dx.doi.org/10.1088/1367-2630/17/2/023068 |
| Access Level: | acceso abierto |
| Palabra clave: | Nonlinear systems Dinamics Time-series analysis complex networks time series analysis nonlinear dynamical systems pseudoperiodic time-series permutation entropy complex network statistical complexity transitions Dinàmica Sistemes no lineals Sèries temporals -- Anàlisi Àrees temàtiques de la UPC :: Física |
| Sumario: | We characterize the evolution of a dynamical system by combining two well-known complex systems' tools, namely, symbolic ordinal analysis and networks. From the ordinal representation of a time series we construct a network in which every node weight represents the probability of an ordinal pattern (OP) to appear in the symbolic sequence and each edge's weight represents the probability of transitions between two consecutive OPs. Several network-based diagnostics are then proposed to characterize the dynamics of different systems: logistic, tent, and circle maps. We show that these diagnostics are able to capture changes produced in the dynamics as a control parameter is varied. We also apply our new measures to empirical data from semiconductor lasers and show that they are able to anticipate the polarization switchings, thus providing early warning signals of abrupt transitions. |
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