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: Journal contributions › Journal articles › Research › peer-review
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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 - Psychology
KW - Average mutual information
KW - Code:Matlab
KW - False-nearest neighbors
KW - Multidimensional Recurrence Quantification Analysis
KW - Multidimensional time series
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 articles
C2 - 30250444
AN - SCOPUS:85053126521
VL - 9
JO - Frontiers in Psychology
JF - Frontiers in Psychology
SN - 1664-1078
IS - SEP
M1 - 1679
ER -