733 research outputs found
Stochastic discrete scale invariance: renormalization group operators and iterated function systems
Abstract We revisit here the notion of discrete scale invariance. Initially defined for signal indexed by the positive reals, we present a generalized version of discrete scale invariant signals relying on a renormalization group approach. In this view, the signals are seen as fixed point of a renormalization operator acting on a space of signal. We recall how to show that these fixed point present discrete scale invariance. As an illustration we use the random iterated function system as generators of random processes of the interval that are dicretely scale invariant
Spectral Analysis of Multi-dimensional Self-similar Markov Processes
In this paper we consider a discrete scale invariant (DSI) process with scale . We consider to have some fix number of
observations in every scale, say , and to get our samples at discrete points
where is obtained by the equality
and . So we provide a discrete time scale
invariant (DT-SI) process with parameter space . We find the spectral representation of the covariance function of
such DT-SI process. By providing harmonic like representation of
multi-dimensional self-similar processes, spectral density function of them are
presented. We assume that the process is also Markov
in the wide sense and provide a discrete time scale invariant Markov (DT-SIM)
process with the above scheme of sampling. We present an example of DT-SIM
process, simple Brownian motion, by the above sampling scheme and verify our
results. Finally we find the spectral density matrix of such DT-SIM process and
show that its associated -dimensional self-similar Markov process is fully
specified by where is
the covariance function of th and th observations of the process.Comment: 16 page
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