How to improve results for river_dataset predicting ahead

Hi everyone, I am a begginer in using matlab neural networks. It would be great if someone could help me to obtain better results. This is my code. Thanks!

%%1. Importing data

load('river_dataset')
T=riverTargets;

%%2. Data preparation

N = 12; % Multi-step ahead prediction

% Input and target series are divided in two groups of data:
% 1st group: used to train the network

targetSeries = T(1:end-N);

% 2nd group: this is the new data used for simulation. inputSeriesVal will 
% be used for predicting new targets. targetSeriesVal will be used for
% network validation after prediction

targetSeriesVal = T(end-N+1:end); % This is generally not available

%%3. Network Architecture

delay = 2;
neuronsHiddenLayer = 10;

% Network Creation

net = narnet(1:delay,neuronsHiddenLayer);
%%4. Training the network

[Xs,Xi,Ai,Ts] = preparets(net,{},{},targetSeries); 
net = train(net,Xs,Ts,Xi,Ai);
view(net)
Y = net(Xs,Xi,Ai); 

% Performance for the series-parallel implementation, only 
% one-step-ahead prediction
perf = perform(net,Ts,Y);

%%5. Multi-step ahead prediction

targetSeriesPred = [targetSeries(end-delay+1:end), con2seq(nan(1,N))];
netc = closeloop(net);
view(netc)
[Xs,Xi,Ai,Ts] = preparets(netc,{},{},targetSeriesPred);
yPred = netc(Xs,Xi,Ai);
perf = perform(net,yPred,targetSeriesVal);
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')

[Greg Heath]

Search on

format tutorial

Then reformat and resubmit.

Answer 1

[Greg Heath]

You are misusing the term validation.

 total = design + test
 design = training + validation

The validation set is part of the design set and helps determine when to stop training. Estimates of performance on nondesign data (i.e., generalization) are obtained using a "holdout" test set.

Apply your data to the help example

 help narnet

Use divideblock to create the training, validation and test sets with uniform spacing.

Later you can see what happens if you try to exclude the validation set.

Initialize the RNG and obtain the training record tr:

 rng(0)
 [ net tr Ys Es Xf Af ] = train(net,Xs,Ts,Xi,Ai);

Search for some of my posts

 greg narnet

Thank you for formally accepting my answer

Greg

[Greg Heath]

This is worth looking at:

http://www.mathworks.com/matlabcentral/answers/77142-future-value-prediction-with-neural-network-method-and-right-input-and-target-format-data

[DavidDj]

Hi Greg,

Thanks for your answer!

Although I have thoroughly read your recommendations I still have not managed to improve my results. I can not figure out how can I use the term validation correctly.

Please, Could you give me some additional advice and more references.