Estimation of the proper embedding dimension for a single-variable time series
Invokes the method of False Nearest Neighbors (FNN) to estimate the minimal embedding dimension of a multivariate data set.
a vector containing a uniformly-sampled real-valued time series.
neighbor tolerance based on attractor size.
If the Euclidean distance between two neighbor candidates is
the maximal embedding dimension. Default:
orbital lag. The number of points along the
trajectory (orbit) of the current point
that must be exceeded in order for
another point in the phase space to be considered
a neighbor candidate. This argument is used
to help attenuate temporal correlation in the
the embedding which can lead to spuriously low
minimal embedding dimension estimates. The orbital lag
must be positive or zero. Default:
false neighbor Euclidean distance tolerance.
If the ratio of the Euclidean distances between neighbor candidates
in successive embedding dimensions exceeds
the time delay between coordinates. Default: the decorrelation time of the autocorrelation function.
an object of class
plots a summary of the results. Available options are:
a character string defining the x-axis label. Default:
a character string defining the y-axis label. Default:
Additional plot arguments (set internally by the
prints a summary of the results. Available options are:
Additional print arguments used by the standard
M. B. Kennel, R. Brown, and H. D. I. Abarbanel (1992), Determining embedding dimension for phase-space reconstruction using a geometrical construction, Physical Review A, 45(6), 3403–3411.
Fredkin, D. R., and Rice, J. A. (1995), Method of false nearest neighbors: A cautionary note, Physical Review E, 51(4), 2950–2954.
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