"I"nput dimension is I=3 and "O"utput dimension is O=8. However, WHAT IS N ??, the length of the total time span? If Ntrn ~ 0.7*N the number of training equations is Ntrneq = Ntrn*O. It is desirable that Ntrneq is not less than the number of parameters that you are trying to estimate Nw = ((NID+1)*I + NFD*O)*H+(H+1)*O.
The only data division option that makes sense for future prediction with constant timesteps is DIVIDEBLOCK!
You might want to start easy using TIMEDELAYNET, NARNET and/or a reduced number of input and output dimensions.
Practice datasets are found using
help nndatasets
doc nndatasets
Please search BOTH the NEWSGROUP & ANSWERS using
greg nncorr narxnet
and see how I estimate the significant correlation lags from the data & Random zero-mean/unit-variance Gaussian Noise of the same length, N.
Consult the posts in reverse chronological order.
HOWEVER, Including the VAL and TEST subsets by using N will optimistically bias your performance results. THEREFORE, ONLY USE THE TRAINING SUBSET OF LENGTH Ntrn to estimate the significant delays!
The form of NNCORR that agrees with the fft of the TRAINING SUBSET POWER SPECTRUM is
crosscorxttrn = nncorr(zxtrn,zttrn,Ntrn-1,'biased');
autocorrttrn = nncorr(zttrn,zttrn,Ntrn-1,'biased');
where
zxtrn = zscore(xtrn',1)';
zttrn = zscore(ttrn',1)';
Are you using TRAINBR because N is not sufficiently large?
Hope this helps.
Greg
