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.

Thanks, and God bless you all! Eric

% 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 1

[Greg Heath]

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

 greg narnet

Check the most recent posts first

Hope this helps

Thank you for formally accepting my answer

Greg

[Eric]

Hello, Greg.. I followed your advice on "help nndatasets".

Demo code doesn't predict One step beyond original data.

Is there anything missing in the demo code? Please, see by yourself:

 % simplenar_dataset Simple time-series prediction dataset

% Single time-series prediction involves predicting the next value of
% a time-series given its past values.

% This dataset can be used to demonstrate how a neural network is
% trained to make predictions.

% LOAD simplenar_dataset.MAT loads this variable:

    % simpleseriesTargets - a 1x100 cell array of scalar values representing
    % a 100 timestep time-series.

% T = simplenar_dataset loads the targets into a variable of your
% choosing.

% For an intro to prediction with the Neural Time Series app
% click "Load Example Data Set" in the second panel and pick this dataset.

% Here is how to design a neural network that predicts the target series
% from past values. See narnet for details.

    T = simplenar_dataset;
    net = narnet(1:2,10);
    [Xs,Xi,Ai,Ts] = preparets(net,{},{},T);
    net = train(net,Xs,Ts,Xi,Ai);
    view(net)
    Y = net(Xs,Xi,Ai)
    plotresponse(Ts,Y)

[Greg Heath]

More clear if you use "o" for openloop (OL) instead of "s".

After designing a successful OL configuration, close the loop to predict beyond the known target region.

Reread the help and doc documentation re converting OL designs to CL designs.

Also search in BOTH NEWSGROUP and ANSWERS for my posts

 greg narnet

Read the most recent posts first.

Greg