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How can predict multi step ahead using Narnet

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coqui
coqui on 14 Jul 2014
Commented: MAT-Magic on 28 Mar 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;
load('prices.mat');
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);
% stepAheadPerformance = perform(net,ts,ys)
how I can predict multistep ahead, for example the prediction of 10 days in advance.
Thanks in advance.
  2 Comments
coqui
coqui on 14 Nov 2014
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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Accepted Answer

Shashank Prasanna
Shashank Prasanna on 14 Jul 2014
Edited: Shashank Prasanna on 14 Jul 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 Comments
omer triyo
omer triyo on 13 May 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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More Answers (2)

Greg Heath
Greg Heath on 17 Jul 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;
load('prices.mat');
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);
% %stepAheadPerformance = perform(net,ts,ys)
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 Comments
MAT-Magic
MAT-Magic on 28 Mar 2020
Thanks Greg, I am reading the posts in your google group :)

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coqui
coqui on 25 Jul 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 Comments
EanX
EanX on 1 Oct 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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