7,297 research outputs found
Time series forecasting by means of evolutionary algorithms
IEEE International Parallel and Distributed Processing Symposium. Long Beach, CA, 26-30 March 2007Many physical and artificial phenomena can be described by time series. The prediction of such phenomenon could be as complex as interesting. There are many time series forecasting methods, but most of them only look for general rules to predict the whole series. The main problem is that time series usually have local behaviours that don't allow forecasting the time series by general rules. In this paper, a new method for finding local prediction rules is presented. Those local prediction rules can attain a better general prediction accuracy. The method presented in this paper is based on the evolution of a rule system encoded following a Michigan approach. For testing this method, several time series domains have been used: a widely known artificial one, the Mackey-Glass time series, and two real world ones, the Venice Lagon and the sunspot time series
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
Potentiostatic infrared titration of 11-Mercaptoundecanoic acid monolayers
Acknowledgment This work was supported by the Spanish DGICYT under grant CTQ2008-00371 and by the Junta de Andalucía under grant P07-FQM-02492.Peer reviewedPostprin
Horizontal Visibility graphs generated by type-I intermittency
The type-I intermittency route to (or out of) chaos is investigated within
the Horizontal Visibility graph theory. For that purpose, we address the
trajectories generated by unimodal maps close to an inverse tangent bifurcation
and construct, according to the Horizontal Visibility algorithm, their
associated graphs. We show how the alternation of laminar episodes and chaotic
bursts has a fingerprint in the resulting graph structure. Accordingly, we
derive a phenomenological theory that predicts quantitative values of several
network parameters. In particular, we predict that the characteristic power law
scaling of the mean length of laminar trend sizes is fully inherited in the
variance of the graph degree distribution, in good agreement with the numerics.
We also report numerical evidence on how the characteristic power-law scaling
of the Lyapunov exponent as a function of the distance to the tangent
bifurcation is inherited in the graph by an analogous scaling of the block
entropy over the degree distribution. Furthermore, we are able to recast the
full set of HV graphs generated by intermittent dynamics into a renormalization
group framework, where the fixed points of its graph-theoretical RG flow
account for the different types of dynamics. We also establish that the
nontrivial fixed point of this flow coincides with the tangency condition and
that the corresponding invariant graph exhibit extremal entropic properties.Comment: 8 figure
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