50 research outputs found
Recurrence-based time series analysis by means of complex network methods
Complex networks are an important paradigm of modern complex systems sciences
which allows quantitatively assessing the structural properties of systems
composed of different interacting entities. During the last years, intensive
efforts have been spent on applying network-based concepts also for the
analysis of dynamically relevant higher-order statistical properties of time
series. Notably, many corresponding approaches are closely related with the
concept of recurrence in phase space. In this paper, we review recent
methodological advances in time series analysis based on complex networks, with
a special emphasis on methods founded on recurrence plots. The potentials and
limitations of the individual methods are discussed and illustrated for
paradigmatic examples of dynamical systems as well as for real-world time
series. Complex network measures are shown to provide information about
structural features of dynamical systems that are complementary to those
characterized by other methods of time series analysis and, hence,
substantially enrich the knowledge gathered from other existing (linear as well
as nonlinear) approaches.Comment: To be published in International Journal of Bifurcation and Chaos
(2011
Quasiperiodic graphs: structural design, scaling and entropic properties
A novel class of graphs, here named quasiperiodic, are constructed via
application of the Horizontal Visibility algorithm to the time series generated
along the quasiperiodic route to chaos. We show how the hierarchy of
mode-locked regions represented by the Farey tree is inherited by their
associated graphs. We are able to establish, via Renormalization Group (RG)
theory, the architecture of the quasiperiodic graphs produced by irrational
winding numbers with pure periodic continued fraction. And finally, we
demonstrate that the RG fixed-point degree distributions are recovered via
optimization of a suitably defined graph entropy
Detecting series periodicity with horizontal visibility graphs
The horizontal visibility algorithm has been recently introduced as a mapping
between time series and networks. The challenge lies in characterizing the
structure of time series (and the processes that generated those series) using
the powerful tools of graph theory. Recent works have shown that the visibility
graphs inherit several degrees of correlations from their associated series,
and therefore such graph theoretical characterization is in principle possible.
However, both the mathematical grounding of this promising theory and its
applications are on its infancy. Following this line, here we address the
question of detecting hidden periodicity in series polluted with a certain
amount of noise. We first put forward some generic properties of horizontal
visibility graphs which allow us to define a (graph theoretical) noise
reduction filter. Accordingly, we evaluate its performance for the task of
calculating the period of noisy periodic signals, and compare our results with
standard time domain (autocorrelation) methods. Finally, potentials,
limitations and applications are discussed.Comment: To be published in International Journal of Bifurcation and Chao
Feigenbaum graphs: a complex network perspective of chaos
The recently formulated theory of horizontal visibility graphs transforms
time series into graphs and allows the possibility of studying dynamical
systems through the characterization of their associated networks. This method
leads to a natural graph-theoretical description of nonlinear systems with
qualities in the spirit of symbolic dynamics. We support our claim via the case
study of the period-doubling and band-splitting attractor cascades that
characterize unimodal maps. We provide a universal analytical description of
this classic scenario in terms of the horizontal visibility graphs associated
with the dynamics within the attractors, that we call Feigenbaum graphs,
independent of map nonlinearity or other particulars. We derive exact results
for their degree distribution and related quantities, recast them in the
context of the renormalization group and find that its fixed points coincide
with those of network entropy optimization. Furthermore, we show that the
network entropy mimics the Lyapunov exponent of the map independently of its
sign, hinting at a Pesin-like relation equally valid out of chaos.Comment: Published in PLoS ONE (Sep 2011
Derivative-variable correlation reveals the structure of dynamical networks
We propose a conceptually novel method of reconstructing the topology of
dynamical networks. By examining the correlation between the variable of one
node and the derivative of another node, we derive a simple matrix equation
yielding the network adjacency matrix. Our assumptions are the possession of
time series describing the network dynamics, and the precise knowledge of the
interaction functions. Our method involves a tunable parameter, allowing for
the reconstruction precision to be optimized within the constraints of given
dynamical data. The method is illustrated on a simple example, and the
dependence of the reconstruction precision on the dynamical properties of time
series is discussed. Our theory is in principle applicable to any weighted or
directed network whose internal interaction functions are known.Comment: Submitted to EPJ
Побудова передвісників кризових явищ засобами рекурентного аналізу складних мереж
Мета статті – провести мережний аналіз часових послідовностей, які включають кризові явища і
встановити, яким чином кількісні характеристики таких мереж змінюються у власне кризові періоди
Complex Networks Unveiling Spatial Patterns in Turbulence
Numerical and experimental turbulence simulations are nowadays reaching the
size of the so-called big data, thus requiring refined investigative tools for
appropriate statistical analyses and data mining. We present a new approach
based on the complex network theory, offering a powerful framework to explore
complex systems with a huge number of interacting elements. Although interest
on complex networks has been increasing in the last years, few recent studies
have been applied to turbulence. We propose an investigation starting from a
two-point correlation for the kinetic energy of a forced isotropic field
numerically solved. Among all the metrics analyzed, the degree centrality is
the most significant, suggesting the formation of spatial patterns which
coherently move with similar vorticity over the large eddy turnover time scale.
Pattern size can be quantified through a newly-introduced parameter (i.e.,
average physical distance) and varies from small to intermediate scales. The
network analysis allows a systematic identification of different spatial
regions, providing new insights into the spatial characterization of turbulent
flows. Based on present findings, the application to highly inhomogeneous flows
seems promising and deserves additional future investigation.Comment: 12 pages, 7 figures, 3 table