NAR-RNN时间序列预测代码（数据集+训练+预测+预测效果对比）
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新手入门必备，可以尝试一下，这里可以直接用自己的数据集替换掉就好了，也可以私信我进行替换！ % Solve an Autoregression Time-Series Problem with a NAR Neural Network% Script generated by Neural Time Series app% Created 01-Mar-2023 19:33:45%% This script assumes this variable is defined:%% y - feedback time series.clcclearload data.maty=data(:,8);T = tonndata(y,false,false); %输入和输出矩阵须为cell类型的矩阵，且不能用num2cell来转换，如果使用二维cell矩阵，将会被认为是两个输入从而不能训练。% Choose a Training Function% For a list of all training functions type: help nntrain% 'trainlm' is usually fastest.% 'trainbr' takes longer but may be better for challenging problems.% 'trainscg' uses less memory. Suitable in low memory situations.trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation. %默认的lmh函数训练时间序列效果很差，采用贝叶斯正则化算法% 对于大多数问题，推荐使用 Levenberg-Marquardt (trainlm)，不过，对于一些含噪小型问题，贝叶斯正则化 (trainbr) 虽然可能需要更长的时间，但会获得更好的解。%但是，对于大型问题，推荐使用量化共轭梯度 (trainscg)，因为它使用的梯度计算比其他两种算法使用的 Jacobian 矩阵计算更节省内存。%由于样本数据特性，此处选择贝叶斯正则化（Bayesian Regularization）。% Create a Nonlinear Autoregressive NetworkfeedbackDelays = 1:20;%延迟向量1:10,即输出参考了前10个值 hiddenLayerSize = 10;net = narnet(feedbackDelays,hiddenLayerSize,'open',trainFcn);%这一项可以来调整神经元的个数，这个需要结合performance的误差来结合调整。一般地，靠增加隐层节点数来获得较低的误差，其训练效果要比增加隐层数更容易实现。%需要注意的是，与在每一层中添加更多的神经元相比，添加层层数将获得更大的性能提升。因此，不要在一个隐藏层中加入过多的神经元。% 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, Testingnet.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);save('net,mat',net)% Test the Networky = net(x,xi,ai);e = gsubtract(t,y);performance = perform(net,t,y)% View the Networkview(net)% Plots% Uncomment these lines to enable various plots.%figure, plotperform(tr)%figure, plottrainstate(tr)%figure, ploterrhist(e)%figure, plotregression(t,y)%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 output layer.netc = closeloop(net);netc.name = [net.name ' - Closed Loop'];view(netc)[xc,xic,aic,tc] = preparets(netc,{},{},T);yc = netc(xc,xic,aic);closedLoopPerformance = 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);nets.name = [net.name ' - Predict One Step Ahead'];view(nets)[xs,xis,ais,ts] = preparets(nets,{},{},T);ys = nets(xs,xis,ais);stepAheadPerformance = perform(nets,ts,ys)