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Calculation of average mutual information (AMI) and false-nearest neighbors (FNN) for the estimation of embedding parameters of multidimensional time-series in Matlab

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Calculation of average mutual information (AMI) and false-nearest neighbors (FNN) for the estimation of embedding parameters of multidimensional time-series in Matlab. / Wallot, Sebastian; Mønster, Dan.

In: Frontiers in Psychology, Vol. 9, No. SEP, 1679, 10.09.2018.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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@article{f757ba030f98442290c610ca252826bf,
title = "Calculation of average mutual information (AMI) and false-nearest neighbors (FNN) for the estimation of embedding parameters of multidimensional time-series in Matlab",
abstract = "Using the method or time-delayed embedding, a signal can be embedded into higher-dimensional space in order to study its dynamics. This requires knowledge of two parameters: The delay parameter t, and the embedding dimension parameter D. Two standard methods to estimate these parameters in one-dimensional time series involve the inspection of the Average Mutual Information (AMI) function and the False Nearest Neighbor (FNN) function. In some contexts, however, such as phase-space reconstruction for Multidimensional Recurrence Quantification Analysis (MdRQA), the empirical time series that need to be embedded already possess a dimensionality higher than one. In the current article, we present extensions of the AMI and FNN functions for higher dimensional time series and their application to data from the Lorenz system coded in Matlab.",
keywords = "BEHAVIOR, CONSTRAINTS, COORDINATION, DELAYS, DIMENSION, DYNAMICS, JOINT ACTION, Multidimensional Recurrence Quantification Analysis, Multidimensional Time series, PHASE-SPACE RECONSTRUCTION, STRANGE ATTRACTORS, SYSTEMS, average mutual information, code:Matlab, false-nearest neighbors, time-delayed embedding",
author = "Sebastian Wallot and Dan M{\o}nster",
year = "2018",
month = "9",
day = "10",
doi = "10.3389/fpsyg.2018.01679",
language = "English",
volume = "9",
journal = "Frontiers in Psychology",
issn = "1664-1078",
publisher = "Frontiers Media S.A",
number = "SEP",

}

RIS

TY - JOUR

T1 - Calculation of average mutual information (AMI) and false-nearest neighbors (FNN) for the estimation of embedding parameters of multidimensional time-series in Matlab

AU - Wallot, Sebastian

AU - Mønster, Dan

PY - 2018/9/10

Y1 - 2018/9/10

N2 - Using the method or time-delayed embedding, a signal can be embedded into higher-dimensional space in order to study its dynamics. This requires knowledge of two parameters: The delay parameter t, and the embedding dimension parameter D. Two standard methods to estimate these parameters in one-dimensional time series involve the inspection of the Average Mutual Information (AMI) function and the False Nearest Neighbor (FNN) function. In some contexts, however, such as phase-space reconstruction for Multidimensional Recurrence Quantification Analysis (MdRQA), the empirical time series that need to be embedded already possess a dimensionality higher than one. In the current article, we present extensions of the AMI and FNN functions for higher dimensional time series and their application to data from the Lorenz system coded in Matlab.

AB - Using the method or time-delayed embedding, a signal can be embedded into higher-dimensional space in order to study its dynamics. This requires knowledge of two parameters: The delay parameter t, and the embedding dimension parameter D. Two standard methods to estimate these parameters in one-dimensional time series involve the inspection of the Average Mutual Information (AMI) function and the False Nearest Neighbor (FNN) function. In some contexts, however, such as phase-space reconstruction for Multidimensional Recurrence Quantification Analysis (MdRQA), the empirical time series that need to be embedded already possess a dimensionality higher than one. In the current article, we present extensions of the AMI and FNN functions for higher dimensional time series and their application to data from the Lorenz system coded in Matlab.

KW - BEHAVIOR

KW - CONSTRAINTS

KW - COORDINATION

KW - DELAYS

KW - DIMENSION

KW - DYNAMICS

KW - JOINT ACTION

KW - Multidimensional Recurrence Quantification Analysis

KW - Multidimensional Time series

KW - PHASE-SPACE RECONSTRUCTION

KW - STRANGE ATTRACTORS

KW - SYSTEMS

KW - average mutual information

KW - code:Matlab

KW - false-nearest neighbors

KW - time-delayed embedding

UR - http://www.scopus.com/inward/record.url?scp=85053126521&partnerID=8YFLogxK

U2 - 10.3389/fpsyg.2018.01679

DO - 10.3389/fpsyg.2018.01679

M3 - Journal article

VL - 9

JO - Frontiers in Psychology

JF - Frontiers in Psychology

SN - 1664-1078

IS - SEP

M1 - 1679

ER -