TIMEDELAYNET predicts future outputs from present and past inputs only
NARNET predicts future outputs from past outputs only
NARXNET predicts future outputs from past outputs in addition to present and past inputs.
Choose the delays from the significant values of the input/output crosscorrelation function at NONEGATIVE lags and output autocorrelation function at POSITIVE lags.
Choose the number of hidden nodes, H, by trial and error. The default is H = 10
[x,t] = simplenarx_dataset;
X = zscore(cell2mat(x));
T = zscore(cell2mat(t));
[ I N ] = size(X)
[ O N ] = size(T)
crosscorrXT = nncorr(X,T,N-1);
autocorrT = nncorr(T,T,N-1);
crosscorrXT(1:N-1) = []; % Delete negative delays
autocorrT(1:N-1) = [];
sigthresh95 = 0.21 % Significance threshold
sigcrossind = crosscorrXT( crosscorrXT >= sigthresh95 )
sigautoind = autocorrT( autocorrT >= sigthresh95 )
inputdelays = sigcrossind(sigcrossind <= 35)
feedbackdelays = sigautoind(sigautoind <= 35)
feedbackdelays(1)=[] % Delete zero delay
If your N = 1000 series is relatively stationary (i.e., the local means and variances are relatively constant) you should be able to reduce the window size below 35 (15?).
Hope this helps.
Thank you for formally accepting my answer.
Greg
