
By recourse to appropriate information theory quantifiers (normalized Shannon entropy and Martín-Plastino-Rosso intensive statistical complexity measure), we revisit the characterization of Gaussian self-similar stochastic processes from a Bandt-Pompe viewpoint. We show that the ensuing approach exhibits considerable advantages with respect to other treatments. In particular, clear quantifiers gaps are found in the transition between the continuous processes and their associated noises.
Time series, Information theory, Continuous-time stochastic process, Shannon entropy, information theory quantifiers, Física, Stochastic process, Martín-Plastino-Rosso intensive statistical complexity, Shannon's source coding theorem, Gaussian, Statistical complexity, Bandt-Pompe, Statistical physics, Measure (mathematics), Data mining, Mathematics
Time series, Information theory, Continuous-time stochastic process, Shannon entropy, information theory quantifiers, Física, Stochastic process, Martín-Plastino-Rosso intensive statistical complexity, Shannon's source coding theorem, Gaussian, Statistical complexity, Bandt-Pompe, Statistical physics, Measure (mathematics), Data mining, Mathematics
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