## Recurrence Plots At A Glance

### Definition

**Recurrence plot** –
A recurrence plot (RP) is an advanced technique of nonlinear data analysis.
It is a visualisation (or a graph) of a square matrix, in which the matrix
elements correspond to those times at which a state of a dynamical system
recurs (columns and rows correspond then to a certain pair of times).
Techniqually, the RP reveals all the times when the phase space trajectory
of the dynamical system visits roughly the same area in the phase space.

» Show animated introduction (Flash)

» Wolfram Demos: (1) Frequency Distribution of the Logistic Map, (2) Recurrence-Based Representations of the Logistic Map, (3) Recurrence Network Measures for the Logistic Map

Natural processes can have a distinct recurrent behaviour, e.g. periodicities (as seasonal or Milankovich cycles), but also irregular cyclicities (as El Niño Southern Oscillation). Moreover, the recurrence of states, in the meaning that states are arbitrary close after some time, is a fundamental property of deterministic dynamical systems and is typical for nonlinear or chaotic systems. The recurrence of states in nature has been known for a long time and has also been discussed in early publications (e.g. recurrence phenomena in cosmic-ray intensity, Monk, 1939).

Eckmann et al. (1987) have introduced a tool which
can visualize the recurrence of states
\(\vec{x}_i\) in a phase
space. Usually,
a phase space does not have a dimension (two or three) which allows
it to be pictured. Higher dimensional phase spaces can
only be visualized by projection into the two or three
dimensional sub-spaces. However, Eckmann's tool
enables us to investigate the \(m\)-dimensional phase space
trajectory through a two-dimensional representation of its
recurrences. Such recurrence of a state at
time \(i\) at a different time
\(j\) is marked within a
two-dimensional squared matrix with ones and zeros dots
(black and white dots in the plot), where
both axes are time axes.
This representation is called *recurrence
plot (RP)*.
Such an RP can be mathematically expressed as
$$
R_{i,j}=\Theta(\varepsilon_i - \|\vec{x}_i - \vec{x}_j\|), \qquad
\vec{x}_i \in \mathbb{R}^m, \quad i,j = 1,\ldots, N,
$$
where \(N\) is the number of considered states \(x_i\),
\(\varepsilon_i\) is a threshold distance,
\(\| \cdot \|\) a norm and \(\Theta( \cdot )\) the Heaviside function.

### Structures in Recurrence Plots

The initial purpose of RPs is the visual inspection of higher dimensional phase space trajectories. The view on RPs gives hints about the time evolution of these trajectories. The advantage of RPs is that they can also be applied to rather short and even nonstationary data.

The RPs exhibit characteristic large scale and small
scale patterns.
The first patterns were denoted by Eckmann et al. (1987) as *typology*
and the latter as *texture*. The typology
offers a global impression which can be characterized as
*homogeneous*, *periodic*, *drift* and *disrupted*.

*Homogeneous*RPs are typical of stationary and autonomous systems in which relaxation times are short in comparison with the time spanned by the RP. An example of such an RP is that of a random time series.- Oscillating systems have RPs with diagonal oriented,
*periodic*recurrent structures (diagonal lines, checkerboard structures). For*quasi-periodic*systems, the distances between the diagonal lines are different. However, even for those oscillating systems whose oscillations are not easily recognizable, the RPs can be used in order to find their oscillations. - The
*drift*is caused by systems with slowly varying parameters. Such slow (adiabatic) change brightens the RP's upper-left and lower-right corners. - Abrupt changes in the dynamics as well as extreme events
cause
*white areas or bands*in the RP. RPs offer an easy possibility to find and to assess extreme and rare events by using the frequency of their recurrences.

The closer inspection of the RPs reveals small scale structures (the texture) which
are *single dots*,
*diagonal lines* as well as *vertical* and *horizontal
lines* (the combination of vertical and horizontal lines obviously forms
rectangular clusters of recurrence points).

*Single, isolated recurrence points*can occur if states are rare, if they do not persist for any time or if they fluctuate heavily. However, they are not a unique sign of chance or noise (for example in maps).- A
*diagonal line*\(R_{i+k, j+k} = 1\) (for \(k=1,\ldots,l\), where \(l\) is the length of the diagonal line) occurs when a segment of the trajectory runs parallel to another segment, i.e. the trajectory visits the same region of the phase space at different times. The length of this diagonal line is determined by the duration of such similar local evolution of the trajectory segments. The direction of these diagonal structures can differ. Diagonal lines parallel to the LOI (angle \(\pi\)) represent the parallel running of trajectories for the same time evolution. The diagonal structures perpendicular to the LOI represent the parallel running with contrary times (mirrored segments; this is often a hint for an inappropriate embedding). Since the definition of the Lyapunov exponent uses the time of the parallel running of trajectories, the relationship between the diagonal lines and the Lyapunov exponent is obvious. - A
*vertical (horizontal) line*\(R_{i,j+k} = 1\) (for \(k=1,\ldots,v\), where \(v\) is the length of the vertical line) marks a time length in which a state does not change or changes very slowly. It seems, that the state is trapped for some time. This is a typical behaviour of laminar states (intermittency).

These small scale structures are the base of a quantitative analysis of the RPs.

Summarizing the last mentioned points we can establish the following list of observations and give the corresponding qualitative interpretation:

Observation | Interpretation |
---|---|

Homogeneity | the process is obviously stationary |

Fading to the upper left and lower right corners | nonstationarity; the process contains a trend or drift |

Disruptions (white bands) occur | nonstationarity; some states are rare or far from the normal; transitions may have occurred |

Periodic/ quasi-periodic patterns | cyclicities in the process; the time distance between periodic patterns (e.g. lines) corresponds to the period; long diagonal lines with different distances to each other reveal a quasi-periodic process |

Single isolated points | heavy fluctuation in the process; if only single isolated points occur, the process may be an uncorrelated random or even anti-correlated process |

Diagonal lines (parallel to the LOI) | the evolution of states is similar at different times; the process could be deterministic; if these diagonal lines occur beside single isolated points, the process could be chaotic (if these diagonal lines are periodic, unstable periodic orbits can be retrieved) |

Diagonal lines (orthogonal to the LOI) | the evolution of states is similar at different times but with reverse time; sometimes this is a sign for an insufficient embedding |

Vertical and horizontal lines/clusters | some states do not change or change slowly for some time; indication for laminar states |

Long bowed line structures | the evolution of states is similar at different epochs but with different velocity; the dynamics of the system could be changing (but note: this is not fully valid for short bowed line structures) |

The visual interpretation of RPs requires some experience. The study of RPs from paradigmatic systems gives a good introduction into characteristic typology and texture. However, their quantification offers a more objective way for the investigation of the considered system. With this quantification, the RPs have become more and more popular within a growing group of scientists who use RPs and their quantification techniques for data analysis (a search with the Scirus search engine in spring 2003 reveals over 200 journal published works and approximately 700 web published works about RPs).

» Recurrence plots in Wikipedia

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