Multi-Step Neural Network - Crude Oil Price Forecasting Model

Hi, I am trying to create a predictive crude oil forecasting model with 2 step ahead prediction.

Following is the code I made but I running into errors. Will really appreciate if you could resolve them. I made facing the error in the final ypred variable.

----------------------------------------------------------------------------------- % Solve an Autoregression Problem with External Input with a NARX Neural Network % Script generated by NTSTOOL % Created Tue May 19 13:44:22 EDT 2015 % % This script assumes these variables are defined: % % Inputs - input time series. % Output - feedback time series.

inputSeries = tonndata(Inputs,true,false); targetSeries = tonndata(Output,true,false);

% Create a Nonlinear Autoregressive Network with External Input

inputDelays = 1:2; feedbackDelays = 1:2; hiddenLayerSize = 20; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);

% Choose Input and 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 % Customize input parameters at: net.inputs{i}.processParam % Customize output parameters at: net.outputs{i}.processParam

net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}; net.inputs{2}.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,inputSeries,{},targetSeries);

% Setup Division of Data for Training, Validation, Testing % The function DIVIDERAND randomly assigns target values to training, % validation and test sets during training. % For a list of all data division functions type: help nndivide

net.divideFcn = 'dividerand'; % Divide data randomly

% The property DIVIDEMODE set to TIMESTEP means that targets are divided % into training, validation and test sets according to timesteps. % For a list of data division modes type: help nntype_data_division_mode

net.divideMode = 'value'; % Divide up every value net.divideParam.trainRatio = 60/100; net.divideParam.valRatio = 25/100; net.divideParam.testRatio = 15/100;

% Choose a Training Function % For a list of all training functions type: help nntrain % Customize training parameters at: net.trainParam

net.trainFcn = 'trainbr'; % Levenberg-Marquardt

% Choose a Performance Function % For a list of all performance functions type: help nnperformance % Customize performance parameters at: net.performParam

net.performFcn = 'mse'; % Mean squared error

% Choose Plot Functions % For a list of all plot functions type: help nnplot % Customize plot parameters at: net.plotParam

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, plotregression(targets,outputs) 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.

netc = closeloop(net); netc.name = [net.name ' - Closed Loop']; view(netc) [xc,xic,aic,tc] = preparets(netc,inputSeries,{},targetSeries); yc = netc(xc,xic,aic); closedLoopPerformance = perform(netc,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.

net = narnet(1:2,1); net.trainParam.showWindow = false;

%T = tonndata(targets, false, false);

[xs,xis,ais,ts] = preparets(nets,inputSeries,{},targetSeries); %net = closeloop(train(net,xs,ts,xis,ais));

ypred = net(xs,xis,ais); ypred = nan(3,1); ypred = tonndata(ypred,false,false);

figure() subplot(3,1,1); plot(cell2mat(outputs),'b','DisplayName','Expected Outputs') hold on plot(cell2mat(targetSeries),'r','DisplayName','Original Targets') title({'Prediction'}) legend('show') hold off

subplot(3,1,2) plot(cell2mat(yc),'b','DisplayName','Expected Outputs with Close Loop') hold on plot(cell2mat(targetSeries),'r','DisplayName','Original Targets') title({'Prediction'}) legend('show') hold off

subplot(3,1,3); plot(cell2mat(ypred),'b','DisplayName','Expected Outputs with Step Ahead Prediction') hold on plot(cell2mat(targetSeries),'r','DisplayName','Original Targets') title({'Prediction'}) legend('show') hold off -----------------------------------------------------------------------------------

Thanks & Regards, Tapas

[Greg Heath]

1. REFORMAT your code so that it can be cut and pasted into the command line

2. Choose a dataset for us to use

 help nndatasets
 doc nndatasets

[Tapas]

Hi Greg,

I didn't get what exactly you want me to change?

Regards, Tapas

[Greg Heath]

The format:

No more than 1 executable statement per line so that the program will run when cut and pasted into the command line.