Extracting features of Gaussian self-similar stochastic processes via the Bandt-Pompe approach

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By recourse to appropriate information theory quantifiers (normalized Shannon entropy and Martin-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 exh...

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Detalles Bibliográficos
Autores principales: Rosso, O. A., Zunino, Luciano José, Pérez, Darío G., Figliola, Alejandra, Larrondo, Hilda A., Garavaglia, Mario José, Martín, María Teresa, Plastino, Ángel Luis
Formato: Articulo
Lenguaje:Inglés
Publicado: 2007
Materias:
Física
Statistical physics
Data mining
Time series
Continuous-time stochastic process
Gaussian
Shannon's source coding theorem
Measure (mathematics)
Statistical complexity
Mathematics
Information theory
Stochastic process
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/126170
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
Descripción
Sumario:By recourse to appropriate information theory quantifiers (normalized Shannon entropy and Martin-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.

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