Hi there, I am using a Narnet for multi-step ahead prediction of a non-linear time series. I read some posts on the newsgroup and wrote a script to train the neural network.
1. About the problem:
* The length of the series is about 1.3M points (i.e. 1 month);* The sampling time of the series is 2 second and * The prediction horizon is 30 steps (i.e. 1 minute);* Only the first 2 hours (3600 points) are used to train the networks.* d=300 and H =3 are chosen after checking the autocorr2. MSE:
* The MSE of the open-loop net, nMSEo, is 2.8211e-04* the MSE of the closed-loop net, nMSEc, is 1.4814* the MSE of the re-trained close-loop net, nMSEcr, is 0.88383. My questions are:
* Is there any errors/misuses on the code/toolbox functions.* Is that normal to have such a huge degradation (>3000times) on the network performance after closing the loop?* Is closed-loop network the only way to make multi-step-ahead-prediction with NARNET.* I am thinking to re-sample the series with a range of sampling rate i.e. 4, 6, 8, ..., 60 seconds, then use parallel open-loop prediction to solve the problem, do you think this is feasible?* since the past data don't have much influence on the current value, should we use the new coming data to re-train the networks on every time interval to update the networks.4. code:
filename=sprintf('data/%d %d.xlsx', month, year); [num,txt,~] = xlsread(filename,sheetname); nStep=size(num,1)-1; nDay=size(txt,2); % use 1 day only
nDay=1; timestamp=num(:,1); data=num(:,(1:nDay)+1); date=txt(1:nDay); signal.date = gadd(timestamp(1:end-1,1),datenum(date,'dd/mm/yyyy')'); signal.date = reshape(signal.date,[nDay*nStep,1]); signal.data = reshape(data(1:end-1,1:end),[nDay*nStep,1]); y=signal'; % use first 2 hours only
y=signal.data(1:2*length(signal)/24)'; Y=num2cell(y); N = length(y); % net param
d = 300; FD = 1:d; H = 3; Horizon = 30; neto = narnet( FD, H ); neto.divideFcn = 'divideblock'; neto.trainParam.showWindow = false; %view(neto)
% preparation
[ Xo, Xoi, Aoi, To ] = preparets( neto, {}, {}, Y ); to = cell2mat( To ); zto = zscore(to,1); % varto1 = mean(var(to',1));
% minmaxto = minmax([ to ; zto ]);
rng( 'default' ) [ neto, tro, Yo, Eo, Aof, Xof ] = train( neto, Xo, To, Xoi, Aoi ); [ Yo, Xof, Aof ] = neto( Xo, Xoi, Aoi ); % evaluate open-loop network
Eo = gsubtract( To, Yo ); perform(neto, To, Yo); %view( neto );
% NMSEo = mse( Eo ) /varto1
NMSEo = mse( Eo ) / var(to,1) yo = cell2mat( Yo ); % plot
plt = plt+1; figure(plt), hold on plot( d+1:N, to); plot( d+1:N, yo, 'r--'); axis( [ 0 N+d -1 1 ] ); legend( 'TARGET', 'OUTPUT' ); title( 'OPENLOOP NARNET RESULTS' );
%%close the loop
[ netc Xci Aci ] = closeloop( neto, Xoi, Aoi );% view( netc )
[ Xc, Xci, Aci, Tc ] = preparets( netc, {}, {}, Y ); if isequal( Tc, To ) tc = to ; % 1
else tc = cell2mat( Tc ); end [ Yc Xcf Acf ] = netc( Xc, Xci, Aci ); % evaluate closed-loop network
Ec = gsubtract( Tc, Yc );% perform(netc, Tc, Yc);
yc = cell2mat( Yc ); NMSEc = mse(Ec) /var(tc,1) plt = plt+1; figure(plt), hold on plot( d+1:N, tc ) plot( d+1:N, yc, 'r--' ) legend('TARGET', 'OUTPUT') title('CLOSELOOP NARNET RESULTS');
%%Re-train
[netc, trc, Ycr, Ecr, Acrf, Xcrf] = train(netc, Xc, Tc, Xci, Aci); [Ycr, Xcrf, Acrf] = netc(Xc, Xci, Aci); Ecr = gsubtract( Tc, Ycr ); perform(netc, Tc, Ycr); ycr = cell2mat(Ycr); NMSEcr = mse(Ecr) /var(tc,1) Xc2 = cell(1,Horizon); [ Yc2 Xcrf Acrf2 ] = netc( Xc2, Xcrf, Acrf ); yc2 = cell2mat(Yc2); plt = plt + 1; figure(plt), hold on plot(d+1:N, tc, 'b') plot(d+1:N, ycr, 'g--') plot( N+1:N+Horizon, yc2, 'ro', 'LineWidth', 2 ) plot( N+1:N+Horizon, yc2, 'r', 'LineWidth', 2 ) legend('TARGET', 'OUTPUT', 'TARGETLESS PREDICTION') % Fix the legend colors
title('TRAINED CLOSELOOP NARNET RESULTS');
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