Influence of observational noise on the recurrence quantification analysis

By M. Thiel, M. Romano, Jürgen Kurths, R. Meucci, E. Allaria, Fortunato Tito Arecchi. 2002

255 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f51aa42da6d317ee47f018ed6d283e1c1138d129

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Norbert Marwan
2. Jürgen Kurths
3. Yong Zou
4. Reik V. Donner
5. Jonathan F. Donges
6. Cornelis J. Stam
7. Tim Oates
8. Zhiguang Wang

In Statistical and Nonlinear Physics

1. Norbert Marwan
2. Yong Zou
3. Reik V. Donner
4. Jonathan F. Donges
5. Jürgen Kurths
6. Cornelis J. Stam
7. Tim Oates
8. Zhiguang Wang

In Signal Processing

1. Norbert Marwan
2. Jürgen Kurths
3. Yong Zou
4. Jonathan F. Donges
5. Reik V. Donner

Citations in the corpus

• A historical review of recurrence plots (2008, background): “They theoretically justified the choice of the recurrence threshold for data with observational noise and were able to analytically describe an RP for noise [51,54].”
• Complex network approaches to nonlinear time series analysis (2019, methodology): “We suggest to use this criterion if weak observational noise is present as it has been found that the choice ε ∼ 5σ is optimal for a wide class of processes [182].”