2018
DOI: 10.1016/j.cnsns.2017.05.018
|View full text |Cite
|
Sign up to set email alerts
|

Recurrence network measures for hypothesis testing using surrogate data: Application to black hole light curves

Abstract: Recurrence networks and the associated statistical measures have become important tools in the analysis of time series data. In this work, we test how effective the recurrence network measures are in analyzing real world data involving two main types of noise, white noise and colored noise. We use two prominent network measures as discriminating statistic for hypothesis testing using surrogate data for a specific null hypothesis that the data is derived from a linear stochastic process. We show that the charac… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
6
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
7
1

Relationship

2
6

Authors

Journals

citations
Cited by 14 publications
(6 citation statements)
references
References 41 publications
0
6
0
Order By: Relevance
“…Recurrence based measures have been applied for the study of black holes (Jacob et al (2018)), variable stars (George et al (2019)), solar radiation (Ogunjo et al (2017)) and exoplanetary systems (Kovács (2019). A rather remarkable advantage of RQA measures is their ability to detect dynamical transitions from a time series even when the details of the underlying dynamics are elusive (Marwan et al (2013); Thiel et al (2004)).…”
Section: Recurrence Based Analysismentioning
confidence: 99%
“…Recurrence based measures have been applied for the study of black holes (Jacob et al (2018)), variable stars (George et al (2019)), solar radiation (Ogunjo et al (2017)) and exoplanetary systems (Kovács (2019). A rather remarkable advantage of RQA measures is their ability to detect dynamical transitions from a time series even when the details of the underlying dynamics are elusive (Marwan et al (2013); Thiel et al (2004)).…”
Section: Recurrence Based Analysismentioning
confidence: 99%
“…Another limitation of the method as presented in this paper is the lack of a reference for determining whether a peak is a potential EWS or merely a random fluctuation. There are several ways to at least check whether the observed peaks are “real”, for example, by repeating the analysis several times on surrogate data (e.g., Jacob et al, 2018 ) or perform a block-randomization on the windowed time course (e.g., Vink et al, 2018 ). Another way to construct a randomization test could be by randomly re-wiring the network layers and re-construct the Multiplex network and evaluate and compare the layer similarities to the observed measures.…”
Section: Discussionmentioning
confidence: 99%
“…We have shown that [37] this critical range is approximately identical for several chaotic systems and white noise and de-pends only on the embedding dimension M. The threshold values used here for the construction of the network are ǫ = 0.06, 0.10, 0.14 and 0.18 for M varying from 2 to 5 respectively. The scheme has been effectively employed to study the influence of noise on the structure of chaotic attractors [38] and for the analysis of light curves from a prominent black hole system [39]. Here we use this scheme for the construction of RN from time series.…”
Section: Recurrence Network and Entropy Measurementioning
confidence: 99%