如何在matlab中绘制NAR预测值

Question

读了几遍之后，我意识到我的项目需要使用NAR，因为我正在使用历史值来预测。我有前几个月的月度数据。&我需要预测未来12个月。

T = tonndata(Target_Set1,false,false);
% Create a Nonlinear Autoregressive Network
feedbackDelays = 1:2;
hiddenLayerSize = 2;
net = narnet(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.
[x,xi,ai,t] = preparets(net,{},{},T);
% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% 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)
% View the Network
view(net)
% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, plotresponse(t,y)
%figure, ploterrcorr(e)
%figure, plotinerrcorr(x,e)
% 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);
[xc,xic,aic,tc] = preparets(netc,{},{},T);
yc = netc(xc,xic,aic);
perfc = perform(net,tc,yc)
% 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);
[xs,xis,ais,ts] = preparets(nets,{},{},T);
ys = nets(xs,xis,ais);
stepAheadPerformance = perform(net,ts,ys)

我如何绘制训练后的预测值和validation.From (我在NARXNET中看到的示例)，通常它们将使用下面的代码。

地块(cell2mat(T)，nan(1，N)；nan(1，length(T))，cell2mat(yPred)； nan(1，长度(T)，cell2mat(TargetSeriesVal))图例(“原始目标”，“网络预测”，“预期输出”)；

提前谢谢。

Answer 1

必须使用高级脚本生成用于预测未来数据的代码。