An upper bound for the proper delay time in chaotic time-series analysis

Ying Cheng Lai; David Lerner; Robert Hayden

doi:10.1016/0375-9601(96)00408-2

An upper bound for the proper delay time in chaotic time-series analysis

Ying Cheng Lai, David Lerner, Robert Hayden

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

We establish an upper bound for the proper delay time in chaotic time series analysis using delay-coordinate embeddings. The derivation is based on analyzing the effective scaling regime in the computation of the correlation dimension using the Grassberger-Procaccia algorithm. Numerical results agree with the theoretical prediction.

Original language	English (US)
Pages (from-to)	30-34
Number of pages	5
Journal	Physics Letters, Section A: General, Atomic and Solid State Physics
Volume	218
Issue number	1-2
DOIs	https://doi.org/10.1016/0375-9601(96)00408-2
State	Published - Jul 29 1996
Externally published	Yes

ASJC Scopus subject areas

Physics and Astronomy(all)

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10.1016/0375-9601(96)00408-2

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abstract = "We establish an upper bound for the proper delay time in chaotic time series analysis using delay-coordinate embeddings. The derivation is based on analyzing the effective scaling regime in the computation of the correlation dimension using the Grassberger-Procaccia algorithm. Numerical results agree with the theoretical prediction.",

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AU - Lerner, David

AU - Hayden, Robert

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N2 - We establish an upper bound for the proper delay time in chaotic time series analysis using delay-coordinate embeddings. The derivation is based on analyzing the effective scaling regime in the computation of the correlation dimension using the Grassberger-Procaccia algorithm. Numerical results agree with the theoretical prediction.

AB - We establish an upper bound for the proper delay time in chaotic time series analysis using delay-coordinate embeddings. The derivation is based on analyzing the effective scaling regime in the computation of the correlation dimension using the Grassberger-Procaccia algorithm. Numerical results agree with the theoretical prediction.

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