Hi. I’m trying to understand the concepts behind finding training strategies for NARNETs that can make as good predictions as possible. What I want to create is a script that I can feed any time series to, regardless of how it looks, and then find the best training design for it. This is the code I have at the moment:
T = simplenar_dataset; %example time series
N = length(T); % length of time series
MaxHidden=10; %number of hidden nodes that will be tested
%Attempt to determine Significant feedback delays with Autocorrelation
autocorrT = nncorr(zscore(cell2mat(T),1),zscore(cell2mat(T),1),N-1);
[ sigacorr inda ] = find(abs(autocorrT(N+1:end) > 0.21))
for hidden=1:MaxHidden
parfor feedbackdelays=1:length(inda)
FD=inda(feedbackdelays);
net = narnet( 1:FD, hidden );
[ Xs, Xsi, Asi, Ts ] = preparets( net, {}, {}, T );
ts = cell2mat( Ts );
net.divideFcn ='divideblock'; %Divides the data using divide block
net.trainParam.min_grad=1e-15;
net.trainParam.epochs=10000;
rng( 'default' )
[ net tr Ys Es Af Xf ] = train( net, Xs, Ts, Xsi, Asi);
NMSEs = mse( Es ) /var( ts,1 )% Mean squared error performance function
performanceDivideBlockNMSEs(hidden,feedbackdelays)=NMSEs;
end
end
Prashant Kumar answered . 2024-05-04 01:05:30
PROOF: a. plot(-(N-1):N-1, autocorrT) b. minmax(autocorrT) = [ -2.3082 1.0134 ] c. sigacorr = ones(1,41)
2. BETTER SOLUTION: Use the Fourier Method
za = zscore(a,1); zb = zscore(b,1); % a,b are double (i.e., not cells) A = fft(za); B = fft(zb); CSDab = A.*conj(B); % Cross Spectral Density crosscorrFab = ifft(CSDab); % F => Fourier method crosscorrFba = conj(crosscorrFab);
3. You might wish to compare this with the NNCORR documentation options
help nncorr doc nncorr % The optional FLAG determines how nncorr normalizes correlations. % 'biased' - scales the raw cross-correlation by 1/N. % 'unbiased' - scales the raw correlation by 1/(N-abs(k)), where k % is the index into the result. % 'coeff' - normalizes the sequence so that the correlations at % zero lag are identically 1.0. % 'none' - no scaling (this is the default). crosscorrBab = nncorr( za, zb, N-1, 'biased' ); % B ==> "b"iased crosscorrNab = nncorr( za, zb, N-1, 'none' )/N; % N ==> "n"one crosscorrUab = nncorr( za, zb, N-1, 'unbiased' ); % U ==> "u"nbiased crosscorrtMab = nncorr( za, zb, N-1 ); % M ==> "m"issing flag % crosscorrCab = nncorr( za, zb, N-1, 'coeff' ); ERROR: BUG
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