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Chaotic dynamics reconstruction from noisy data: Phenomenon of predictability worsening for incomplete set of observables

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  • Berczyñski, Stefan
  • Kravtsov, Yury A.
  • Anosov, Oleg

Abstract

The phenomenon of predictability worsening is studied, which is characteristic for chaotic dynamics, reconstructed from incomplete set of observational data. It is pointed out that in conditions of data deficiency, when there are fewer observables than independent variables, reconstruction procedure inevitably has to deal with additional differentiations of noisy observables, which is the main reason for the phenomenon of predictability worsening to take place, especially in the presence of short-correlated noise.

Suggested Citation

  • Berczyñski, Stefan & Kravtsov, Yury A. & Anosov, Oleg, 2009. "Chaotic dynamics reconstruction from noisy data: Phenomenon of predictability worsening for incomplete set of observables," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1459-1466.
    • Handle: RePEc:eee:chsofr:v:41:y:2009:i:3:p:1459-1466
    DOI: 10.1016/j.chaos.2008.06.007
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    References listed on IDEAS

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    1. He, Qie & Wang, Ling & Liu, Bo, 2007. "Parameter estimation for chaotic systems by particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 34(2), pages 654-661.
    2. Chang, Wei-Der, 2006. "Parameter identification of Rossler’s chaotic system by an evolutionary algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 29(5), pages 1047-1053.
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