how can i test an time series NAR NN forecasting tool?

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Dear all,
i would like to predicte the wind speed of a spesific location for the next 2 hours (12 points of 10min data) for that i am only using lagged input data of the time series (NAR AA tool).
i am trying to test the optimum number of delays (lagged inputs) needed to get a good prediction resutls using a for loop to increse the number of delays (for example between 1 and 10)
I am also trying to test the optimum number of inputs (input length) that need to be used for training the network and getting a better prediction. also by using another for loop (for example betweem number_of_delay_plus_one to length(input))
my code looks like the following:
if true
clear
clc
load('C:\Users\00037218\Desktop\Wind Speed prediction\E82_Wind_1_2013.mat');
% Solve an Autoregression Time-Series Problem with a NAR Neural Network
% Script generated by NTSTOOL
% Created Fri Mar 28 22:27:25 CET 2014
%
% This script assumes this variable is defined:
%
% Target_2 - feedback time series.
if 1 == 0
prediction_indx = 12;
time_input = Input_wind(:,1);
time_target = (time_input(end)+mean(diff(time_input)):mean(diff(time_input)):time_input(end)+(prediction_indx*mean(diff(time_input))))';
time = [time_input;time_target];
Final = [time Final_test];
i_end = length(Final_test);
j_end = 10;
for i = prediction_indx:prediction_indx:i_end
targetSeries = tonndata(Final_test(1:i,:),false,false);
targetSeriesVal = tonndata(Final_test(i+1:end,:),false,false);
for j = 1:i-1%j_end
feedbackDelays = 1:j;
hiddenLayerSize = prediction_indx;
net = narnet(feedbackDelays,hiddenLayerSize);
% Choose Feedback Pre/Post-Processing Functions
% Settings for feedback input are automatically applied to feedback output
% For a list of all processing functions type: help nnprocess
% TAKE CARE THIS LINE WAS ACTIVE: net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
% Prepare the Data for Training and Simulation
% The function PREPARETS prepares timeseries data for a particular network,
% shifting time by the minimum amount to fill input states and layer states.
% Using PREPARETS allows you to keep your original time series data unchanged, while
% easily customizing it for networks with differing numbers of delays, with
% open loop or closed loop feedback modes.
[inputs,inputStates,layerStates,targets] = preparets(net,{},{},targetSeries);
% Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivide
if 1 == 1
net.divideFcn = 'dividerand'; % Divide data randomly
net.divideMode = 'time'; % Divide up every value
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 10/100;
net.divideParam.testRatio = 20/100;
else
net.divideFcn = 'divideblock'; % Divide data in blocks
net.divideMode = 'time'; % Divide up every value
end
% Choose a Training Function
% For a list of all training functions type: help nntrain
net.trainFcn = 'trainlm'; % Levenberg-Marquardt
% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
net.performFcn = 'mse'; % Mean squared error
% Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','plotresponse', ...
'ploterrcorr', 'plotinerrcorr'};
% Train the Network
[net,tr] = train(net,inputs,targets,inputStates,layerStates);
% Test the Network
outputs = net(inputs,inputStates,layerStates);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)
% Recalculate Training, Validation and Test Performance
trainTargets = gmultiply(targets,tr.trainMask);
valTargets = gmultiply(targets,tr.valMask);
testTargets = gmultiply(targets,tr.testMask);
trainPerformance = perform(net,trainTargets,outputs)
valPerformance = perform(net,valTargets,outputs)
testPerformance = perform(net,testTargets,outputs)
% View the Network
%view(net)
% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, plotresponse(targets,outputs)
%figure, ploterrcorr(errors)
%figure, plotinerrcorr(inputs,errors)
% Closed Loop Network
% Use this network to do multi-step prediction.
% The function CLOSELOOP replaces the feedback input with a direct
% connection from the outout layer.
% ----------------------------------------------------------------
delay=length(feedbackDelays); N=prediction_indx;
targetSeriesPred = [targetSeries(end-delay+1:end), con2seq(nan(1,N))];
% ----------------------------------------------------------------
netc = closeloop(net);
[xc,xic,aic,tc] = preparets(netc,{},{},targetSeriesPred);
yc = netc(xc,xic,aic);
perfc = perform(net,tc,yc);
% Early 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);
[xs,xis,ais,ts] = preparets(nets,{},{},targetSeries);
ys = nets(xs,xis,ais);
closedLoopPerformance = perform(net,tc,yc);
figure();
subplot(3,1,1);
plot(cell2mat(inputs)); hold on;plot(cell2mat(outputs),'-.r');grid on; hold off;
legend('Original Targets (Inputs)','Network Predictions (output)')
subplot(3,1,2);
plot(cell2mat(targets)); hold on;plot(cell2mat(yc),'-.r');grid on; hold off;
legend('Original Targets','Network Predictions')
subplot(3,1,3);
plot(cell2mat(targetSeriesPred)); hold on;plot(cell2mat(yc),'-.r');grid on; hold off;
legend('Original Targets_pred.','Network Predictions')
save('Input_Data.mat','targetSeries','targetSeriesVal','i','j','prediction_indx')
clear
load('Input_Data.mat');
end
end
end
my question is:
1- what i am doing wrong:
a- why my target data is changing even that i need it to be constant which mean i will always try to predicte the same next 2 hours with different delays used. (but i am not)
b- how can i use the same input data length for training with changable 'for loop' number of lags (delays)?
c- how to re-do the calcultion on a new input data length which is also changable 'for loop' in numaber
So simply speaking i am trying to do the following (only example):
if true
for i = 5:5: length(inputdata)
NN_input = inputdata(1:i);
NN_target = inputdata(i+1:12);% 12 is for the next 2 hours it can be changed and it is
% only used to compare
forj = 1:1:10 % for changing the delays in the delay layer
feedbackDelays = 1:j;
* _% in this part my input and target data should stay the same *They are no*_ *
% network code
% stuff for testing the resutls
% stuff for presenting the reustls
end
end
Thx in advance for you support

采纳的回答

Greg Heath
Greg Heath 2014-4-20
Find feedback delays from the significant delays of the target autocorrelation function.
Find input delays from the significant delays of the input/target cross-correlation function.
Search the NEWSGROUP and ANSWERS using
greg nncorr narnet
Hope this helps.
Thank you for formally accepting my answer
Greg
  3 个评论
Greg Heath
Greg Heath 2014-4-23
All of this is covered in previous NEWSGROUP and ANSWERS posts
greg nncorr narnet
What have I done in my posts that you don't do in yours?
Aubai
Aubai 2014-4-25
hallo Greg,
thanks for your answer :)
I am trying to find the answer in those previous Answers, Newsgroup. but not fining it can you refer me to a spesific one?
another question: how the 'preparets' function works? i think my problem is there as iam getting random input data everytime the for-loop changes and this giving me not what i am expecting.
again i would like to: for the same available input data (for training) train the network for different delays number (changing with for-loop) and then predict the next 12 points in my time-series. so in my case i want the 'preparets' function to give me always the same set of input, validation and training data yet with different number of delays. how can i do this??
Best Regards

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