Before blindly using ANY time series function, determine which delays are significant.
[ O N ] = size(t); O = 1
[I N ] = size(x); I >=1
zsct1 = zscore(t,1);
zscx1 = zscore(x',1)';
Now use xcorr, crosscorr (which I do not have) or nncorr to calculate and plot, correlation functions to help determine the significant positive delays of
1. The output autocorrelation function (correlation of current output with past outputs)
autocorrt = nncorr(zsct1,zsct1,N-1,'biased');
autocorrt(positive delays) = autocorrt(N+1:2*N-1);
2. Positive delays of the input/output crosscorrelation functions (correlation of current input with past outputs)
crosscorrtx1 = nncorr(zsct1,zscx1(1,:),N-1,'biased');
crosscorrtx2 = nncorr(zsct1,zscx1(2,:),N-1,'biased');
...
crosscorrxtI = nncorr(zsct1,zscx1(I,:),N-1,'biased');
However, the time series neural nets do not allow different delays for
different components of the input.
Hope this helps.
Thank you for formally accepting my answer.
Greg
