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 clearclcload('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
Best Answer