Local random analogue prediction of nonlinear processes

Francesco Paparella, A. Provenzale, L. A. Smith, C. Taricco, R. Vio

Research output: Contribution to journalArticle

Abstract

Given that is not possible to predict the precise evolution of either stochastic processes or chaotic processes from observations, a data-based algorithm with minimal model-structure constraints is presented for generating stochastic series which are realistic, in that their long-term statistics reflect those of a process consistent with the observations. This approach employs random analogues, and complements that of deterministic nonlinear prediction which estimates an expected value. Contrasting these approaches clarifies the distinction between Lorenz's predictions of the first and second kind. Output from several nonlinear stochastic processes and observations of quasar 3C 345 are analysed; the synthetic time series have power spectra, amplitude distributions and intermittency properties similar to those of the observations.

Original languageEnglish (US)
Pages (from-to)233-240
Number of pages8
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Volume235
Issue number3
DOIs
StatePublished - Nov 3 1997

Fingerprint

analogs
stochastic processes
predictions
intermittency
complement
quasars
power spectra
statistics
output
estimates

Keywords

  • Deterministic systems
  • Dynamical reconstruction
  • Nonlinear prediction
  • Stochastic systems
  • Time series analysis
  • Variability of astrophysical and geophysical systems

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Local random analogue prediction of nonlinear processes. / Paparella, Francesco; Provenzale, A.; Smith, L. A.; Taricco, C.; Vio, R.

In: Physics Letters, Section A: General, Atomic and Solid State Physics, Vol. 235, No. 3, 03.11.1997, p. 233-240.

Research output: Contribution to journalArticle

Paparella, Francesco ; Provenzale, A. ; Smith, L. A. ; Taricco, C. ; Vio, R. / Local random analogue prediction of nonlinear processes. In: Physics Letters, Section A: General, Atomic and Solid State Physics. 1997 ; Vol. 235, No. 3. pp. 233-240.
@article{642297a76e2c446ab4f720263bb4e0b6,
title = "Local random analogue prediction of nonlinear processes",
abstract = "Given that is not possible to predict the precise evolution of either stochastic processes or chaotic processes from observations, a data-based algorithm with minimal model-structure constraints is presented for generating stochastic series which are realistic, in that their long-term statistics reflect those of a process consistent with the observations. This approach employs random analogues, and complements that of deterministic nonlinear prediction which estimates an expected value. Contrasting these approaches clarifies the distinction between Lorenz's predictions of the first and second kind. Output from several nonlinear stochastic processes and observations of quasar 3C 345 are analysed; the synthetic time series have power spectra, amplitude distributions and intermittency properties similar to those of the observations.",
keywords = "Deterministic systems, Dynamical reconstruction, Nonlinear prediction, Stochastic systems, Time series analysis, Variability of astrophysical and geophysical systems",
author = "Francesco Paparella and A. Provenzale and Smith, {L. A.} and C. Taricco and R. Vio",
year = "1997",
month = "11",
day = "3",
doi = "10.1016/S0375-9601(97)00607-5",
language = "English (US)",
volume = "235",
pages = "233--240",
journal = "Physics Letters, Section A: General, Atomic and Solid State Physics",
issn = "0375-9601",
publisher = "Elsevier",
number = "3",

}

TY - JOUR

T1 - Local random analogue prediction of nonlinear processes

AU - Paparella, Francesco

AU - Provenzale, A.

AU - Smith, L. A.

AU - Taricco, C.

AU - Vio, R.

PY - 1997/11/3

Y1 - 1997/11/3

N2 - Given that is not possible to predict the precise evolution of either stochastic processes or chaotic processes from observations, a data-based algorithm with minimal model-structure constraints is presented for generating stochastic series which are realistic, in that their long-term statistics reflect those of a process consistent with the observations. This approach employs random analogues, and complements that of deterministic nonlinear prediction which estimates an expected value. Contrasting these approaches clarifies the distinction between Lorenz's predictions of the first and second kind. Output from several nonlinear stochastic processes and observations of quasar 3C 345 are analysed; the synthetic time series have power spectra, amplitude distributions and intermittency properties similar to those of the observations.

AB - Given that is not possible to predict the precise evolution of either stochastic processes or chaotic processes from observations, a data-based algorithm with minimal model-structure constraints is presented for generating stochastic series which are realistic, in that their long-term statistics reflect those of a process consistent with the observations. This approach employs random analogues, and complements that of deterministic nonlinear prediction which estimates an expected value. Contrasting these approaches clarifies the distinction between Lorenz's predictions of the first and second kind. Output from several nonlinear stochastic processes and observations of quasar 3C 345 are analysed; the synthetic time series have power spectra, amplitude distributions and intermittency properties similar to those of the observations.

KW - Deterministic systems

KW - Dynamical reconstruction

KW - Nonlinear prediction

KW - Stochastic systems

KW - Time series analysis

KW - Variability of astrophysical and geophysical systems

UR - http://www.scopus.com/inward/record.url?scp=0005777198&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0005777198&partnerID=8YFLogxK

U2 - 10.1016/S0375-9601(97)00607-5

DO - 10.1016/S0375-9601(97)00607-5

M3 - Article

AN - SCOPUS:0005777198

VL - 235

SP - 233

EP - 240

JO - Physics Letters, Section A: General, Atomic and Solid State Physics

JF - Physics Letters, Section A: General, Atomic and Solid State Physics

SN - 0375-9601

IS - 3

ER -