734 research outputs found
Revival of oscillation from mean-field-induced death : Theory and experiment
Submitted ACKNOWLEDGMENTS T. B. acknowledges the financial support from SERB, Department of Science and Technology (DST), India [Project Grant No.: SB/FTP/PS-005/2013]. D. G. acknowledges DST, India, for providing support through the INSPIRE fellowship. J. K. acknowledges Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with Institute of Applied Physics RAS).Peer reviewedPreprin
Recurrence Plots 25 years later -- gaining confidence in dynamical transitions
Recurrence plot based time series analysis is widely used to study changes
and transitions in the dynamics of a system or temporal deviations from its
overall dynamical regime. However, most studies do not discuss the significance
of the detected variations in the recurrence quantification measures. In this
letter we propose a novel method to add a confidence measure to the recurrence
quantification analysis. We show how this approach can be used to study
significant changes in dynamical systems due to a change in control parameters,
chaos-order as well as chaos-chaos transitions. Finally we study and discuss
climate transitions by analysing a marine proxy record for past sea surface
temperature. This paper is dedicated to the 25th anniversary of the
introduction of recurrence plots
Optimal model-free prediction from multivariate time series
Forecasting a time series from multivariate predictors constitutes a
challenging problem, especially using model-free approaches. Most techniques,
such as nearest-neighbor prediction, quickly suffer from the curse of
dimensionality and overfitting for more than a few predictors which has limited
their application mostly to the univariate case. Therefore, selection
strategies are needed that harness the available information as efficiently as
possible. Since often the right combination of predictors matters, ideally all
subsets of possible predictors should be tested for their predictive power, but
the exponentially growing number of combinations makes such an approach
computationally prohibitive. Here a prediction scheme that overcomes this
strong limitation is introduced utilizing a causal pre-selection step which
drastically reduces the number of possible predictors to the most predictive
set of causal drivers making a globally optimal search scheme tractable. The
information-theoretic optimality is derived and practical selection criteria
are discussed. As demonstrated for multivariate nonlinear stochastic delay
processes, the optimal scheme can even be less computationally expensive than
commonly used sub-optimal schemes like forward selection. The method suggests a
general framework to apply the optimal model-free approach to select variables
and subsequently fit a model to further improve a prediction or learn
statistical dependencies. The performance of this framework is illustrated on a
climatological index of El Ni\~no Southern Oscillation.Comment: 14 pages, 9 figure
Analyzing long-term correlated stochastic processes by means of recurrence networks: Potentials and pitfalls
Long-range correlated processes are ubiquitous, ranging from climate
variables to financial time series. One paradigmatic example for such processes
is fractional Brownian motion (fBm). In this work, we highlight the potentials
and conceptual as well as practical limitations when applying the recently
proposed recurrence network (RN) approach to fBm and related stochastic
processes. In particular, we demonstrate that the results of a previous
application of RN analysis to fBm (Liu \textit{et al.,} Phys. Rev. E
\textbf{89}, 032814 (2014)) are mainly due to an inappropriate treatment
disregarding the intrinsic non-stationarity of such processes. Complementarily,
we analyze some RN properties of the closely related stationary fractional
Gaussian noise (fGn) processes and find that the resulting network properties
are well-defined and behave as one would expect from basic conceptual
considerations. Our results demonstrate that RN analysis can indeed provide
meaningful results for stationary stochastic processes, given a proper
selection of its intrinsic methodological parameters, whereas it is prone to
fail to uniquely retrieve RN properties for non-stationary stochastic processes
like fBm.Comment: 8 pages, 6 figure
Collective Relaxation Dynamics of Small-World Networks
Complex networks exhibit a wide range of collective dynamic phenomena,
including synchronization, diffusion, relaxation, and coordination processes.
Their asymptotic dynamics is generically characterized by the local Jacobian,
graph Laplacian or a similar linear operator. The structure of networks with
regular, small-world and random connectivities are reasonably well understood,
but their collective dynamical properties remain largely unknown. Here we
present a two-stage mean-field theory to derive analytic expressions for
network spectra. A single formula covers the spectrum from regular via
small-world to strongly randomized topologies in Watts-Strogatz networks,
explaining the simultaneous dependencies on network size N, average degree k
and topological randomness q. We present simplified analytic predictions for
the second largest and smallest eigenvalue, and numerical checks confirm our
theoretical predictions for zero, small and moderate topological randomness q,
including the entire small-world regime. For large q of the order of one, we
apply standard random matrix theory thereby overarching the full range from
regular to randomized network topologies. These results may contribute to our
analytic and mechanistic understanding of collective relaxation phenomena of
network dynamical systems.Comment: 12 pages, 10 figures, published in PR
Complex Network Approach to the Statistical Features of the Sunspot Series
Complex network approaches have been recently developed as an alternative
framework to study the statistical features of time-series data. We perform a
visibility-graph analysis on both the daily and monthly sunspot series. Based
on the data, we propose two ways to construct the network: one is from the
original observable measurements and the other is from a
negative-inverse-transformed series. The degree distribution of the derived
networks for the strong maxima has clear non-Gaussian properties, while the
degree distribution for minima is bimodal. The long-term variation of the
cycles is reflected by hubs in the network which span relatively large time
intervals. Based on standard network structural measures, we propose to
characterize the long-term correlations by waiting times between two subsequent
events. The persistence range of the solar cycles has been identified over
15\,--\,1000 days by a power-law regime with scaling exponent
of the occurrence time of the two subsequent and successive strong minima. In
contrast, a persistent trend is not present in the maximal numbers, although
maxima do have significant deviations from an exponential form. Our results
suggest some new insights for evaluating existing models. The power-law regime
suggested by the waiting times does indicate that there are some level of
predictable patterns in the minima.Comment: 18 pages, 11 figures. Solar Physics, 201
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