ghkss {RTisean}R Documentation

Noise reduction

Description

Performs a noise reduction, through an orthogonal projection onto manifold using an euclidean or a special metric.

Usage

ghkss(series, l, x = 0, c = 1, m = 5, d = 1, q = 3, k = 30, r, i = 1, two = FALSE)

Arguments

series a vector or a matrix.
l number of data to use.
x number of lines to be ignored.
c column to be read.
m embedding dimension.
d delay for the embedding.
q dimension of the manifold to project to.
k minimal number of neighbours.
r minimal size of the neighbourhood.
i number of iterations.
two use euclidean metric instead of the special one.

Value

A list of vectors with filtered time series, one per iteration.

References

P. Grassberger, R. Hegger, H. Kantz, C. Schaffrath, and T. Schreiber, On noise reduction methods for chaotic data, CHAOS 3, 127 (1993).

See Also

project

Examples

## Not run: 

#FIXME: find better example
dat <- logistic(iter=10000,r=3.8, noisesd=0.01)
filteredat <- ghkss(dat)[[1]]
delayedfiltered <- embed(filteredat, d=2)
delayed <- embed(dat,d=2)
plot(delayed,cex=0.8,xlab="x(t)",ylab="x(t+1)",main="Delayed Logistic Chaotic Attractor",pch='.')
points(delayedfiltered,col=2,cex=0.8,pch='.')
legend(0.5,0.4, c("Noisy data","Filtered data"),fill=c(1,2), bty="n",cex=0.8)

## End(Not run)

[Package RTisean version 3.0.10 Index]