Vega del Valle, I. and Schütte, Ch. and Conrad, T. O. F.
(2016)
*Finding metastable states in real-world time series with recurrence networks.*
Physica A, 445
.

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## Abstract

In the framework of time series analysis with recurrence networks, we introduce a self-adaptive method that determines the elusive recurrence threshold and identifies metastable states in complex real-world time series. As initial step, we introduce a way to set the embedding parameters used to reconstruct the state space from the time series. We set them as the ones giving the maximum Shannon entropy of the diagonal line length distribution for the first simultaneous minima of recurrence rate and Shannon entropy. To identify metastable states, as well as the transitions between them, we use a soft partitioning algorithm for module finding which is specifically developed for the case in which a system shows metastability. We illustrate our method with a complex time series example. Finally, we show the robustness of our method for identifying metastable states. Our results suggest that our method is robust for identifying metastable states in complex time series, even when introducing considerable levels of noise and missing data points.

Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences > Mathematics > Applied Mathematics Mathematical and Computer Sciences > Mathematics > Mathematical Methods |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics > Comp. Proteomics Group Department of Mathematics and Computer Science > Institute of Mathematics Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group |

ID Code: | 1467 |

Deposited By: | Admin Administrator |

Deposited On: | 25 Nov 2014 13:38 |

Last Modified: | 14 Apr 2016 07:21 |

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