### Abstract

Parallel to the rapid development of nonlinear dynamics, there has been a tremendous amount of effort devoted to data analysis. Suppose an experiment is conducted and some time series are measured. Such a time series can be, for instance, a voltage signal from a physical or a biological experiment, or the concentration of a substance in a chemical reaction, or the amount of instantaneous traffic at a point in the Internet, and so on. The general question is this: what can we say about the underlying dynamical system that generates the time series if the equations governing the time evolution of the system are unknown and the only available information about the system is a set of measured time series?.

Original language | English (US) |
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Title of host publication | Applied Mathematical Sciences (Switzerland) |

Publisher | Springer |

Pages | 413-434 |

Number of pages | 22 |

DOIs | |

State | Published - Jan 1 2011 |

### Publication series

Name | Applied Mathematical Sciences (Switzerland) |
---|---|

Volume | 173 |

ISSN (Print) | 0066-5452 |

ISSN (Electronic) | 2196-968X |

### Fingerprint

### Keywords

- Chaotic Attractor
- Lyapunov Exponent
- Periodic Orbit
- Positive Lyapunov Exponent
- Topological Entropy

### ASJC Scopus subject areas

- Applied Mathematics

### Cite this

*Applied Mathematical Sciences (Switzerland)*(pp. 413-434). (Applied Mathematical Sciences (Switzerland); Vol. 173). Springer. https://doi.org/10.1007/978-1-4419-6987-3_12

**Transient Chaotic Time-Series Analysis.** / Lai, Ying-Cheng; Tél, Tamás.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Applied Mathematical Sciences (Switzerland).*Applied Mathematical Sciences (Switzerland), vol. 173, Springer, pp. 413-434. https://doi.org/10.1007/978-1-4419-6987-3_12

}

TY - CHAP

T1 - Transient Chaotic Time-Series Analysis

AU - Lai, Ying-Cheng

AU - Tél, Tamás

PY - 2011/1/1

Y1 - 2011/1/1

N2 - Parallel to the rapid development of nonlinear dynamics, there has been a tremendous amount of effort devoted to data analysis. Suppose an experiment is conducted and some time series are measured. Such a time series can be, for instance, a voltage signal from a physical or a biological experiment, or the concentration of a substance in a chemical reaction, or the amount of instantaneous traffic at a point in the Internet, and so on. The general question is this: what can we say about the underlying dynamical system that generates the time series if the equations governing the time evolution of the system are unknown and the only available information about the system is a set of measured time series?.

AB - Parallel to the rapid development of nonlinear dynamics, there has been a tremendous amount of effort devoted to data analysis. Suppose an experiment is conducted and some time series are measured. Such a time series can be, for instance, a voltage signal from a physical or a biological experiment, or the concentration of a substance in a chemical reaction, or the amount of instantaneous traffic at a point in the Internet, and so on. The general question is this: what can we say about the underlying dynamical system that generates the time series if the equations governing the time evolution of the system are unknown and the only available information about the system is a set of measured time series?.

KW - Chaotic Attractor

KW - Lyapunov Exponent

KW - Periodic Orbit

KW - Positive Lyapunov Exponent

KW - Topological Entropy

UR - http://www.scopus.com/inward/record.url?scp=84901233988&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901233988&partnerID=8YFLogxK

U2 - 10.1007/978-1-4419-6987-3_12

DO - 10.1007/978-1-4419-6987-3_12

M3 - Chapter

AN - SCOPUS:84901233988

T3 - Applied Mathematical Sciences (Switzerland)

SP - 413

EP - 434

BT - Applied Mathematical Sciences (Switzerland)

PB - Springer

ER -