# How can predict multi step ahead using Narnet

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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

John D'Errico
on 14 Jul 2014

### Accepted Answer

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
on 13 May 2017

Edited: omer triyo
on 13 May 2017

### More Answers (2)

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

Greg Heath
on 24 Jul 2014

Search the NEWSGROUP and ANSWERS using combinations of

greg

timedelaynet, narnet or narxnet

nncorr

Hub, Hmin:dH:Hmax

Hope this helps.

Greg

coqui
on 25 Jul 2014

##### 26 Comments

Greg Heath
on 7 Aug 2015

Start with a subset containing the first few. If that doesn't work, add more.

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
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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