Question

我必须使用NAR网络来训练我的项目的时间序列。为了了解时间序列工具（ntstool）如何在 MATLAB 中工作，我在matlab中使用了 ntstool 的GUI，其中包含 chickenpoxTargets的示例数据集包含一个元素的498个时间步长。在训练时，我使用了一个带有10隐藏层和delay value = 5的神经网络。 GUI生成以下代码：

% Solve an Autoregression Time-Series Problem with a NAR Neural Network
% Script generated by NTSTOOL
% Created Mon May 12 10:47:14 IST 2014
%
% This script assumes this variable is defined:
%
%   chickenpoxTargets - feedback time series.

targetSeries = chickenpoxTargets;

% Create a Nonlinear Autoregressive Network
feedbackDelays = 1:5;
hiddenLayerSize = 10;
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.
[inputs,inputStates,layerStates,targets] = preparets(net,{},{},targetSeries);

% 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,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, 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);
[xc,xic,aic,tc] = preparets(netc,{},{},targetSeries);
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)

现在我有以下三个问题：

GUI中的**延迟值（d）**是什么意思。这是否意味着在训练网络时假设每个时间步长值都依赖于最后'd'时间步长的值？
在未来的时间步长预测值的语法是什么？
当我使用sim(results.net,input)模拟网络时，results.net是神经网络，输入是预测输出的输入行向量，无论输入行向量是什么，我都得到相同的输出。更具体地说，输出不同的输入向量：
```
sim(results.net,[956])

ans =

  136.8790

sim(results.net,[957])

ans =

  136.8790

sim(results.net,[954,966])

ans =

  136.8790  136.8790
```

为什么我得到相同的输出？

Answer 1

延迟参数表示您插入模型的时间步长

y(t) = f(y(t-1), y(t-2), ... y(t-d))

因此，每个时间步长取决于最后d时间步长

要预测更多时间步而不插入新数据，您必须将模型更改为closedloop模型。

表示在脚本中显示：

% convert from openloop to closedloop
netc = closeloop(net);
% prepare your data for the network (scaling!!)
[xc,xic,aic,tc] = preparets(netc,{},{},targetSeries);
% calculate new output
yc = netc(xc,xic,aic);
% get performance of network
perfc = perform(net,tc,yc)

您必须在使用net之前准备输入数据（使用 preparets ）。正如MATLAB Documentation sim()中所示，需要几个参数：
```
[Y,Xf,Af] = sim(net,X,Xi,Ai,T)
```
此外：

...通过将神经网络对象（网络）作为函数调用来隐式调用sim函数。

意味着：您可以使用net()，如2.所示，与sim()
相同
如果没有缩放，您的输入与从中学到的网络不同。因此输出错误的数字。

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