Takes two data sets and fits a zeroth order model of data set 1 (X1) to predict data set 2 (X2) - cross prediction. It then computes the error of the model. This is done by searching for all neighbors in X1 of the points of set X2 which should be forecasted and taking as their images the average of the images of the neighbors. The obtained forecast error is normalized to the variance of data set X2.
Inputs
Both X1 and X2 must be present. They must be realvectors of the same length.
Parameters
Embedding dimension [default = 3].
Delay for embedding [default = 1].
The number of points for which the error should be calculated [default = all].
Minimum number of neighbors for the fit [default = 30].
The neighborhood size to start with [default = 1e-3].
Factor by which to increase the neighborhood size if not enough neighbors were found [default = 1.2].
Steps to be forecast (x2(n+steps) = av(x1(i+steps)
) [default = 1].
Output
Contains value of parameter ’s’ lines. Each line represents the forecast error divided by the standard deviation of the second data set (X2). This second data set is the one being forecasted.
Algorithms
The algorithms for this functions have been taken from the TISEAN package.
Package: tisean