We connect a common conventional value to quantify a recurrence plot with its motifs, which have recently been termed “recurrence triangles.” The common practical value we focus on is DET, which is the ratio of the points forming diagonal line segments of length 2 or longer within a recurrence plot. As a topological value, we use different recurrence triangles defined previously. As a measure-theoretic value, we define the typical recurrence triangle frequency dimension, which generally fluctuates around 1 when the underlying dynamics are governed by deterministic chaos. By contrast, the dimension becomes higher than 1 for a purely stochastic system. Additionally, the typical recurrence triangle frequency dimension correlates most precisely with DET among the above quantities. Our results show that (i) the common practice of using DET could be partly theoretically supported using recurrence triangles, and (ii) the variety of recurrence triangles behaves more consistently for identifying the strength of stochasticity for the underlying dynamics. The results in this study should be useful in checking basic properties for modeling a given time series.

1.
J.-P.
Eckmann
,
S. O.
Kamphorst
, and
D.
Ruelle
, “
Recurrence plots of dynamical systems
,”
Europhys. Lett.
4
,
973
977
(
1987
).
2.
N.
Marwan
,
M. C.
Romano
,
M.
Thiel
, and
J.
Kurths
, “
Recurrence plots for the analysis of complex systems
,”
Phys. Rep.
438
,
237
329
(
2007
).
3.
J. P.
Zbilut
and
C. L.
Webber
, Jr.
, “
Embeddings and delays as derived from quantification of recurrence plots
,”
Phys. Lett. A
171
,
199
203
(
1992
).
4.
C. L.
Webber
, Jr.
and
J. P.
Zbilut
, “
Dynamical assessment of physiological systems and states using recurrence plot strategies
,”
J. Appl. Physiol.
76
,
965
973
(
1994
).
5.
Y.
Hirata
, “
Recurrence plots for characterizing random dynamical systems
,”
Commun. Nonlinear Sci. Numer. Simul.
94
,
105552
(
2021
).
6.
M.
Sano
,
S.
Sato
, and
Y.
Sawada
, “
Global spectral characterization of chaotic dynamics
,”
Prog. Theor. Phys.
76
,
945
948
(
1986
).
7.
R. M.
May
, “
Simple mathematical models with very complicated dynamics
,”
Nature
261
,
459
467
(
1976
).
8.
M.
Hénon
, “
A two-dimensional mapping with a strange attractor
,”
Commun. Math. Phys.
50
,
69
77
(
1976
).
9.
E. N.
Lorenz
, “
Deterministic non-periodic flow
,”
J. Atmos. Sci.
20
,
130
141
(
1963
).
10.
O. E.
Rössler
, “
An equation for continuous chaos
,”
Phys. Lett.
57A
,
397
398
(
1976
).
11.
T.
Sauer
, “
Reconstruction of dynamical systems from interspike intervals
,”
Phys. Rev. Lett.
72
,
3811
1814
(
1994
).
12.
E. N.
Lorenz
, “Predictability: A problem partly solved,” in Proceedings of the Seminar on Predictability (ECMWF, 19960), Vol. 1, pp. 1–18.
13.
J. A.
Hansen
and
L. A.
Smith
, “
The role of operational constraints in selecting supplementary observations
,”
J. Atmos. Sci.
57
,
2859
2871
(
2000
).
14.
J. D.
Hamilton
,
Time Series Analysis
(
Princeton University Press
,
1994
).
15.
C. G.
Lamoureux
and
W. D.
Lastrapes
, “
Persistence in variance, structural change, and the GARCH model
,”
J. Bus. Econ. Stat.
8
,
225
234
(
1990
); available at https://www.tandfonline.com/doi/ref/10.1080/07350015.1990.10509794?scroll=top&role=tab.
16.
K.
Matsumoto
and
I.
Tsuda
, “
Noise-induced order
,”
J. Stat. Phys.
31
,
87
106
(
1983
).
17.
F.
Takens
, “
Detecting strange attractors in turbulence
,”
Lect. Notes Math.
898
,
366
381
(
1981
).
18.
T.
Sauer
,
J. A.
Yorke
, and
M.
Casdagli
, “
Embedology
,”
J. Stat. Phys.
65
,
579
616
(
1991
).
19.
J. S.
Iwanski
and
E.
Bradley
, “
Recurrence plots of experimental data: To embed or not to embed?
,”
Chaos
8
,
861
871
(
1998
).
20.
R. L.
Devaney
,
An Introduction to Chaotic Dynamical Systems
,
2nd ed.
(
Addison-Wesley
,
1989
).
21.
Y.
Hirata
and
K.
Aihara
, “
Devaney’s chaos on recurrence plots
,”
Phys. Rev. E
82
,
036209
(
2010
).
22.
N.
Marwan
,
N.
Wessel
,
U.
Meyerfeldt
,
A.
Schirdewan
, and
J.
Kurths
, “
Recurrence-plot-based measures of complexity and their application to heart-rate-variability data
,”
Phys. Rev. E
66
,
026702
(
2002
).
23.
G.
Corso
,
T. L.
Prado
,
G. Z.
dos Santos Lima
,
J.
Kurths
, and
S. R.
Lopes
, “
Quantifying entropy using recurrence matrix microstates
,”
Chaos
28
,
083108
(
2018
).
24.
T. L.
Prado
,
B. R. R.
Boaretto
,
G.
Corso
,
G. Z.
dos Santos Lima
,
J.
Kurths
, and
S. R.
Lopes
, “
A direct method to detect deterministic and stochastic properties of data
,”
New J. Phys.
24
,
033027
(
2022
).
25.
P.
beim Graben
and
A.
Hutt
, “
Detecting recurrence domains of dynamical systems by symbolic dynamics
,”
Phys. Rev. Lett.
110
,
154101
(
2013
).
26.
J. P.
Crutchfield
and
N. H.
Packard
, “
Symbolic dynamics of one-dimensional maps: Entropies, finite precision, and noise
,”
Int. J. Theor. Phys.
21
,
433
466
(
1982
).
27.
J. M.
Amigó
and
M. B.
Kennel
, “
Topological permutation entropy
,”
Physica D
231
,
137
142
(
2007
).
28.
K.
Aihara
,
T.
Takabe
, and
M.
Toyoda
, “
Chaotic neural networks
,”
Phys. Lett. A
144
,
333
340
(
1990
).
29.
Y.
Hirata
and
K.
Aihara
, “
Estimating optimal partitions for stochastic complex systems
,”
Eur. Phys. J. Spec. Top.
222
,
303
315
(
2013
).
30.
Y.
Hirata
and
J. M.
Amigó
, “
A review of symbolic dynamics and symbolic reconstruction of dynamical systems
,”
Chaos
33
,
052101
(
2023
).
31.
J. D.
Victor
and
K. P.
Purpura
, “
Metric-space analysis of spike trains: Theory, algorithms and application
,”
Netw.: Comput. Neural Syst.
8
,
127
164
(
1997
).
32.
E. P.
Schoenberg
and
K. E.
Tranbarger
, “
Description of earthquake aftershock sequences using prototype point patterns
,”
Environmetrics
19
,
271
286
(
2008
).
33.
S.
Suzuki
,
Y.
Hirata
, and
K.
Aihara
, “
Definition of distance for marked point process data and its application to recurrence plot-based analysis of exchange tick data of foreign currencies
,”
Int. J. Bifurcat. Chaos
20
,
3699
3708
(
2010
).
34.
Y.
Hirata
and
N.
Sukegawa
, “
Two efficient calculations of edit distance between marked point processes
,”
Chaos
29
,
101107
(
2019
).
35.
A.
Banerjee
,
B.
Goswami
,
Y.
Hirata
,
D.
Eroglu
,
B.
Merz
,
J.
Kurths
, and
N.
Marwan
, “
Recurrence analysis of extreme event-like data
,”
Nonlinear Processes Geophys.
28
,
213
229
(
2021
).
36.
M. C. W.
van Rossum
, “
A novel spike distance
,”
Neural Comput.
13
,
751
763
(
2001
).
37.
T.
Kreuz
,
J. S.
Haas
,
A.
Morelli
,
H. D. I.
Abarbanel
, and
A.
Politi
, “
Measuring spike train synchrony
,”
J. Neurosci. Methods
165
,
151
161
(
2007
).
38.
H.
Hino
,
K.
Takano
, and
N.
Murata
, “
mmpp: A package for calculating similarity and distance metrics for simple and marked temporal point processes
,”
R. J.
7
,
237
248
(
2015
).
39.
K.
Iwayama
,
Y.
Hirata
, and
K.
Aihara
, “
Definition of distance for nonlinear time series analysis of marked point process data
,”
Phys. Lett. A
381
,
257
2762
(
2017
).
40.
T. M.
Cover
and
J. A.
Thomas
,
Elements of Information Theory
(
John Wiley & Sons, Inc.
,
1991
).

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