Find the optimal embedding dimension by means of false nearest neighbours.
y=fnn(x) y=fnn(x,m) y=fnn(x,m,t) y=fnn(x,m,t,r,s) fnn(...) fnn(...,param)
y=fnn(x) computes the vector y of the amount of false nearest neighbours (FNN) as a function of the embedding dimension.
y=fnn(x,m), where m is a scalar, computes the FNN up to dimension m. The defeault is m=10.
y=fnn(x,m,t), where t is a scalar, computes the FNN using embedding delay t. The defeault is t=1.
y=fnn(x,m,t,r,s), where r and s are scalars, applies the neighbourhood criterion r and the size of the neighbourhood s. the defeault is r=15 and s=Inf.
y=fnn(x,m,t,r,s,n), where n is a scalar, uses n random samples for the determination of the FNNs. This speeds up the estimation, especially for long data series. The default is n=length(x) if the data length is smaller than 5000, else n=4000.
fnn(...) without any output arguments opens a GUI for interactively changing the parameters.
By using the GUI, the FNNs can be stored into the workspace.
fnn without any arguments calls a demo (the same as the example below).
|Additional parameters according to the GUI.|
|'gui'||-||Creates the GUI.|
|'nogui'||-||Suppresses the GUI.|
|'silent'||-||Suppresses all output.|