The analysis of chaotic time-series data

Eric Kostelich

doi:10.1016/S0167-6911(97)00045-5

The analysis of chaotic time-series data

Eric Kostelich

Mathematical and Statistical Sciences, School of (SoMSS)

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

14 Scopus citations

Abstract

This paper presents a brief survey of time-series analysis methods that are applicable to processes whose behavior can be described as low-dimensional chaos. The goal of these methods is to allow experimentalists to obtain local estimates of the dynamics directly from a set of data. These estimates are often sufficiently accurate to attempt noise reduction, prediction, and control.

Original language	English (US)
Pages (from-to)	313-319
Number of pages	7
Journal	Systems and Control Letters
Volume	31
Issue number	5
DOIs	https://doi.org/10.1016/S0167-6911(97)00045-5
State	Published - Oct 10 1997

Keywords

Chaotic dynamics
Embedding
Time series

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science
Mechanical Engineering
Electrical and Electronic Engineering

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10.1016/S0167-6911(97)00045-5

Cite this

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abstract = "This paper presents a brief survey of time-series analysis methods that are applicable to processes whose behavior can be described as low-dimensional chaos. The goal of these methods is to allow experimentalists to obtain local estimates of the dynamics directly from a set of data. These estimates are often sufficiently accurate to attempt noise reduction, prediction, and control.",

keywords = "Chaotic dynamics, Embedding, Time series",

author = "Eric Kostelich",

note = "Funding Information: This work has been supported in part by the US Department of Energy Office of Scientific Computing under grant number DE-FG03-94ER25213.",

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N2 - This paper presents a brief survey of time-series analysis methods that are applicable to processes whose behavior can be described as low-dimensional chaos. The goal of these methods is to allow experimentalists to obtain local estimates of the dynamics directly from a set of data. These estimates are often sufficiently accurate to attempt noise reduction, prediction, and control.

AB - This paper presents a brief survey of time-series analysis methods that are applicable to processes whose behavior can be described as low-dimensional chaos. The goal of these methods is to allow experimentalists to obtain local estimates of the dynamics directly from a set of data. These estimates are often sufficiently accurate to attempt noise reduction, prediction, and control.

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KW - Embedding

KW - Time series

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The analysis of chaotic time-series data

Abstract

Keywords

ASJC Scopus subject areas

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