How forecast One step ahead (N+1) with NARNET?

Hello people! Please, can you help me?

 

I want predict One Step beyond original data using NARNET. Original data has 62000 steps. Want know the 62001.

 

But what I get for prediction is the exact same existing steps in training data. (Same N steps, same curves)

 

Why network can't predict next future step beyond original data end? I used Removedelay. (Want the N+1)

 

Attached are: Original data (EURUSD), performance stats. Bellow, the code used.

 

 

% Solve an Autoregression Time-Series Problem with a NAR Neural Network
% Script generated by Neural Time Series app
% This script assumes this variable is defined:
%   EURUSD - feedback time series.

 T = tonndata(EURUSD,true,false);

 % Choose a Training Function
trainFcn = 'trainbr';  % Bayesian Regularization backpropagation.

 % Create a Nonlinear Autoregressive Network
feedbackDelays = 1:1;
hiddenLayerSize = 30;
net = narnet(feedbackDelays,hiddenLayerSize,'open',trainFcn);

 % Choose Feedback Pre/Post-Processing Functions
net.input.processFcns = {'removeconstantrows','mapminmax'};
net.trainParam.min_grad = 3e-8;

 % Prepare the Data for Training and Simulation
[x,xi,ai,t] = preparets(net,{},{},T);

 % Setup Division of Data for Training, Validation, Testing
net.divideFcn = 'dividetrain';
net.divideMode = 'time';  % Divide up every sample

 % Choose a Performance Function
net.performFcn = 'mse';  % Mean Squared Error

 % Choose Plot Functions
net.plotFcns = {'plotperform','plottrainstate', 'ploterrhist', ...
    'plotregression', 'plotresponse', 'ploterrcorr', 'plotinerrcorr'};

 % Train the Network
[net,tr] = train(net,x,t,xi,ai);

 % Test the Network
y = net(x,xi,ai);
e = gsubtract(t,y);
performance = perform(net,t,y)

 % Step-Ahead Prediction Network
% For some applications it helps to get the prediction a timestep early.
% The original network returns predicted y(t+1) at the same time it is
% given y(t+1). For some applications such as decision making, it would
% help to have predicted y(t+1) once y(t) is available, but before the
% actual y(t+1) occurs. The network can be made to return its output a
% timestep early by removing one delay so that its minimal tap delay is now
% 0 instead of 1. The new network returns the same outputs as the original
% network, but outputs are shifted left one timestep.

 nets = removedelay(net);
nets.name = [net.name ' - Predict One Step Ahead'];
view(nets)
[xs,xis,ais,ts] = preparets(nets,{},{},T);
ys = nets(xs,xis,ais);
stepAheadPerformance = perform(nets,ts,ys)

 

ANSWER

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Do not use the REMOVEDELAY command

 

 It is not necessary

 and

 it is too confusing.

If you need detailed help, use one of the MATLAB example sets

 help nndatasets

and/or

 doc nndatasets.

Almost anything you need to do is in a former post. Try searching in BOTH the NEWSGROUP and ANSWERS using

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