Predict 2014 Housing Price with Neural networks Toolbox using Times Series

Question

WT 2014-10-17

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https://ww2.mathworks.cn/matlabcentral/answers/159017-predict-2014-housing-price-with-neural-networks-toolbox-using-times-series

编辑： WT 2014-10-17

Hi, I have trained a network using x(t) (9 rows with 96 columns - data from 1990 to 2013) and y(t) (1 row 96 columns) with ntstool. I have saved the simple script that it has generated but I realized that I can only get the predicted values of the targeted output from (1992-2013). May I know what I have to do to predict the output for 2014? Do I have to add anything in the simple script generated?

Here is the resulting script file: % Solve an Autoregression Problem with External Input with a NARX Neural Network % Script generated by NTSTOOL % Created Thu Oct 09 15:32:05 SGT 2014 % % This script assumes these variables are defined: % % CPI_IN - input time series. % CPI_Target_OUT - feedback time series.

inputSeries = tonndata(CPI_IN,true,false); targetSeries = tonndata(CPI_Target_OUT,true,false);

% Create a Nonlinear Autoregressive Network with External Input inputDelays = 1:4; feedbackDelays = 1:4; hiddenLayerSize = 10; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);

% 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 net.divideParam.trainRatio = 75/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 10/100;

% 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)

% 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. nets = removedelay(net); nets.name = [net.name ' - Predict One Step Ahead']; view(nets) [xs,xis,ais,ts] = preparets(nets,inputSeries,{},targetSeries); ys = nets(xs,xis,ais); earlyPredictPerformance = perform(nets,ts,ys)