# How can predict multi step ahead using Narnet

7 visualizaciones (últimos 30 días)
coqui el 14 de Jul. de 2014
Comentada: MAT-Magic el 28 de Mzo. de 2020
I want to predict the future prices, I have used only the daily historical prices as input. I can predict only one step ahead using this code:
clear all;
clear all;
set_size = 1413;
targetSeries =prices(1:set_size);
targetSeries = targetSeries';
targetSeries_train = targetSeries(1:set_size * (4/5) );
targetSeries_test = targetSeries(set_size * (4/5): end);
targetSeries_train = num2cell(targetSeries_train);
targetSeries_test = num2cell(targetSeries_test);
feedbackDelays = 1:4;
hiddenLayerSize = 10;
net = narnet(feedbackDelays, hiddenLayerSize);
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
[inputs, inputStates, layerStates,targets] = preparets(net,{},{}, targetSeries_train);
[inputs_test,inputStates_test,layerStates_test,targets_test] = preparets(net,{},{},targetSeries_test);
net.trainFcn = 'trainrp'; % Levenberg-Marquardt
net.performFcn = 'mse'; % Mean squared error
net.plotFcns = {'plotperform','plottrainstate','plotresponse', ...
'ploterrcorr', 'plotinerrcorr'};
[net,tr] = train(net,inputs,targets,inputStates,layerStates);
outputs = net(inputs_test,inputStates_test,layerStates_test);
errors = gsubtract(targets_test,outputs);
performance = perform(net,targets_test,outputs)
view(net)
% netc = closeloop(net);
% [xc,xic,aic,tc] = preparets(netc,{},{},targetSeries_test);
% yc = netc(xc,xic,aic);
% perfc = perform(net,tc,yc)
% nets = removedelay(net);
% [xs,xis,ais,ts] = preparets(nets,{},{},targetSeries_test);
% ys = nets(xs,xis,ais);
how I can predict multistep ahead, for example the prediction of 10 days in advance.
##### 2 comentariosMostrar NingunoOcultar Ninguno
John D'Errico el 14 de Jul. de 2014
Argh! Learn how to use the code formatting tool. I'll fix it this once. See that your code is unreadable as it WAS, but after one click of the mouse it is now readable.
coqui el 14 de Nov. de 2014
Editada: Walter Roberson el 6 de Ag. de 2015
Dear friend,
I found the feedbackdelays=1, I want to verify if my presented code is true.
To predict multistep ahead, for example the prediction of 10 days in advance:
% net = narnet(1:1,10);
% net.trainParam.showWindow = false;
% T = tonndata(Y,false,false);
% [Xs,Xi,Ai,Ts] = preparets(net,{},{},T);
% net = closeloop(train(net,Xs,Ts,Xi,Ai));
% Ypred = nan(11,1);
% Ypred = tonndata(Ypred,false,false);
% Ypred(1:1) = T(end:end);
%[xc,xic,aic,tc] = preparets(net,{},{},Ypred);
% Ypred = fromnndata(net(xc,xic,aic),true,false,false);
Thank you very much.

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Shashank Prasanna el 14 de Jul. de 2014
Editada: Shashank Prasanna el 14 de Jul. de 2014
Take a look at the line where I define Ypred here I specify nans for the number of forecasts I want to make. I specify 4 more for pre-samples. Hope this helps. And ofcourse Y is your data Ypred are the forecasts
net = narnet(1:4,10);
net.trainParam.showWindow = false;
T = tonndata(Y,false,false);
[Xs,Xi,Ai,Ts] = preparets(net,{},{},T);
net = closeloop(train(net,Xs,Ts,Xi,Ai));
Ypred = nan(14,1);
Ypred = tonndata(Ypred,false,false);
Ypred(1:4) = T(end-3:end);
[xc,xic,aic,tc] = preparets(net,{},{},Ypred);
Ypred = fromnndata(net(xc,xic,aic),true,false,false);
##### 9 comentariosMostrar 7 comentarios más antiguosOcultar 7 comentarios más antiguos
coqui el 19 de Jul. de 2014
I found this code but for narxnet:
figure;
plot([cell2mat(targetSeries),nan(1,N);
nan(1,length(targetSeries)),cell2mat(yPred);
nan(1,length(targetSeries)),cell2mat(targetSeriesVal)]')
legend('Original Targets','Network Predictions','Expected Outputs')
I need your help to plot this figure for narnet.
Thanks.
omer triyo el 13 de Mayo de 2017
Editada: omer triyo el 13 de Mayo de 2017
hi, When i try to predict future values, i got same values by increasing number of forecast. for example, i try to predict future values for 365 days. I got same values after about 150 days. What can i do ? Thanks.

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### Más respuestas (2)

Greg Heath el 17 de Jul. de 2014
%0. a. I do not have MATLAB on this computer, so some of my code comments may need correcting.
% b. I will not complain if you decide to change your mind and accept my answer
% c. Did you mean to begin with close all instead of two clears?
clear all;
clear all;
set_size = 1413;
targetSeries = prices(1:set_size);
targetSeries = targetSeries';
targetSeries_train = targetSeries(1:set_size * (4/5) );
targetSeries_test = targetSeries(set_size * (4/5): end);}
%1. setsize*4/5 is not an integer
% 2. Why are you trying to avoid the default validation set used to avoid overtraining an overfit net (i.e., more unknown weights than training equations)?
% 3. WARNING: You will have to override the default net.divideFcn = 'dividerand' and associated net.divide... trn/val/tst ratios 0.7/0.15/0.15. Use net.divideFcn = 'dividetrain' with the default ratios 100/0/0. I don't think any other option allows absense of a val set.
%4. HOWEVER, my recommendation is to use net.divideFcn = 'divideblock' and accept the default ratios 0.7/0.15/0.15 . Otherwise is more trouble than it's worth. You will automatically get all three results at once.
targetSeries_train = num2cell(targetSeries_train);
targetSeries_test = num2cell(targetSeries_test);
% 5. OK, but check out function tonndata
feedbackDelays = 1:4;
%6. Use the significant delays indicated by the autocorrelation function. For examples search greg narnet nncorr
hiddenLayerSize = 10;
% 7. Default value of 10 may not work well. However, it is good for preliminary investigation because Numweights = ( 4+1)*10+(10+1)*1= 61 << Numtrainequations = 1130
net = narnet(feedbackDelays, hiddenLayerSize);
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
% 8. Unnecessary this is a default
[inputs, inputStates, layerStates,targets] = preparets(net,{},{}, targetSeries_train);
[inputs_test,inputStates_test,layerStates_test,targets_test] = preparets(net,{},{}, targetSeries_test);
net.trainFcn = 'trainrp'; % Levenberg-Marquardt
%9. INCORRECT. 'trainlm' is L-M;
net.performFcn = 'mse'; % Mean squared error
net.plotFcns = {'plotperform','plottrainstate','plotresponse', 'ploterrcorr', 'plotinerrcorr'};
% 10. Last 3 statements are defaults. Can omit to simplify code
[net,tr] = train(net,inputs,targets,inputStates,layerStates);
% 11. INCORRECT . You did not change the defaults net.divideFcn = 'dividerand' and net.divideRatio = 0.7/0.15/0.15
outputs = net(inputs_test,inputStates_test,layerStates_test);
errors = gsubtract(targets_test,outputs);
performance = perform(net,targets_test,outputs)
view(net)
netc = closeloop(net);
[xc,xic,aic,tc] = preparets(netc,{},{},targetSeries_test);
%%yc = netc(xc,xic,aic);
perfc = perform(net,tc,yc)
%12. Often closing the loop so degrades performance that you have to train netc
%%nets = removedelay(net);
% %[xs,xis,ais,ts] = preparets(nets,{},{},targetSeries_test);
% %ys = nets(xs,xis,ais);
how I can predict multistep ahead, for example the prediction of 10 days in advance.
% 13. I DON'T UNDERSTAND the use of removedelay. The significant delays of the autocorrelation function indicate how far you can predict ahead with probabilistic certainty. So 1<= FD <= dmax and the values may be nonconsecutive. I think removedelay removes the lower values of FD with, I assume, a decrease in performance. When my computer is available I will investigate.
%Hope this helps.
%Thank you for formally accepting my answer
Greg
##### 8 comentariosMostrar 6 comentarios más antiguosOcultar 6 comentarios más antiguos
Greg Heath el 24 de Jul. de 2014
Search the NEWSGROUP and ANSWERS using combinations of
greg
timedelaynet, narnet or narxnet
nncorr
Hub, Hmin:dH:Hmax
Hope this helps.
Greg
MAT-Magic el 28 de Mzo. de 2020

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coqui el 25 de Jul. de 2014
Thank you Greg,
I find these expressions to compute Hub:
1) Hub = -1 + ceil( (Ntrneq-O) / (MXFD*O + O +1) )
2) Hub = -1 + ceil( ( Ntrneq-O)/ (O +1))
The right expression is 1 or 2?
##### 26 comentariosMostrar 24 comentarios más antiguosOcultar 24 comentarios más antiguos
Greg Heath el 7 de Ag. de 2015
If you plot the autocorreltion function, the two threshold lines, and color the points outside of the lines red, you will get a much better idea of what is going on. Similarly for crosscorrelations.
EanX el 1 de Oct. de 2015
To obtain 10 steps-ahead forecast, I have to use an empty cell array as input to closed loop net or a cell array of NaN? I suppose that both methods will work so a tried this code:
clearvars; clc; close all;
% test with a simple dataset
Y=simplenar_dataset;
net = narnet(1:4,3);
net.trainParam.showWindow = false;
T = Y(1:80);%tonndata(Y,false,false);
% use only first 80 samples to train and the remaining 20
% to test predictions
Tpred=Y(81:end);
[Xs,Xi,Ai,Ts] = preparets(net,{},{},T);
% is necessary to add Xi and Ai to closeloop to have xic1 and aci1
[net, xic1, aic1] = closeloop(train(net,Xs,Ts,Xi,Ai),Xi,Ai);
% NaN method, require to set first 4 (equal to delay) values
Ypred = nan(14,1);
Ypred = tonndata(Ypred,false,false);
Ypred(1:4) = T(end-3:end);
% empty cells method
Xc1=cell(1,10);
[xc,xic,aic,tc] = preparets(net,{},{},Ypred);
% prediction resulting from the two methods does not agree
% empty cell does not work but perhaps I'm missing something?
% empty cells method
Ypred1 = fromnndata(net(Xc1,xic1,aic1),true,false,false);
Ypred = fromnndata(net(xc,xic,aic),true,false,false); % NaN method
% plot results
plot([Ypred Ypred1 cell2mat(Tpred(1:10))']);
legend('Pred-NaN','Pred-EmptyCells','Target');
But I obtain different results. What am I missing? Thanks.

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