错误使用 reshape 元素数不能更改。请使用 [] 作为大小输入之一，以自动计算该维度的适当大小。

第38句trainNetWork报错显示了这句话，不知道哪里出了问题，完整代码如下

date21=readmatrix('CD1410.xlsx');
GDP1=date21(:,2);
figure(1);
subplot(1,1,1);
plot(GDP1,'-ob','LineWidth',1.5,'MarkerSize',3);
title('原始DC14-DC10');
hold on
datayuce=GDP1;
out=[];
for i=1:1
dateTrain=datayuce(:,i);
mu= mean(dateTrain);
sig= std(dateTrain);
dateTrainStandardized =(dateTrain -mu)/ sig;
XTrain=dateTrainStandardized(1:end-1);
YTrain=dateTrainStandardized(2:end);
numFeatures =1;
numResponses =1;
numHiddenUnits =250;
layers=[sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits)
regressionLayer];
options=trainingOptions('adam', ...
'MaxEpochs',500, ...
'InitialLearnRate',0.005, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropPeriod',120, ...
'LearnRateDropFactor',0.25, ...
'Verbose',0,...
'plots','training-progress');
XTrain =XTrain';
YTrain =YTrain';
net = trainNetwork(XTrain,YTrain,layers,options);
net = predictAndUpdateState(net,XTrain);
[net,YPred]=predictAndUpdateState(net,YTrain(end));
numTimeStepsTest= 30;
for i= 2:numTimeStepsTest
[net,YPred(:,i)]=predictAndUpdateState(net,YPred(:,i-1),'ExecutionEnvironment','auto');
end
YPredout{i}=sig*YPred+mu;
out=[out YPredout{i}'];
end
figure(2);
plot(GDP1,'-ob','LineWidth',1.5,'MarkerSize',3);
hold on
plot(366:1:395,out(:,1)','LineWidth',1.5,'MarkerSize',3,'LineStyle','*','MarkerEdgeColor','P')

根据参考资料中的第0段，错误信息显示"错误使用reshape，元素数不能更改。请使用 [] 作为大小输入之一，以自动计算该维度的适当大小。"是由于使用reshape时调整后的元素个数与原先的不一致导致的。根据提供的代码，问题可能出现在trainNetWork函数中的某个地方。首先我们需要检查输入的大小是否正确。

请尝试修改代码如下所示：

function []=trainNetWork(XTrain,YTrain,layers,options,i)
    net=layelongconnect(layers, 'lstm');

    XTrain = reshape(XTrain,[],1,1,size(XTrain,1));
    YTrain = reshape(YTrain,[],1,1,size(YTrain,1));

    net = trainNetwork(XTrain,YTrain,layers,options);
end

date21=readmatrix('CD1410.xlsx');
GDP1=date21(:,2);
figure(1);
subplot(1,1,1);
plot(GDP1,'-ob','LineWidth',1.5,'MarkerSize',3);
title('原始DC14-DC10');
hold on
datayuce=GDP1;
out=[];
for i=1:1
    dateTrain=datayuce(:,i);
    mu=mean(dateTrain);
    sig=std(dateTrain);
    dateTrainStandardized =(dateTrain -mu)/ sig;
    XTrain=dateTrainStandardized(1:end-1);
    YTrain=dateTrainStandardized(2:end);
    numFeatures =1;
    numResponses =1;
    numHiddenUnits =250;
    layers=[sequenceInputLayer(numFeatures)
            lstmLayer(numHiddenUnits)
            regressionLayer];
    options=trainingOptions('adam', ...
                            'MaxEpochs',500, ...
                            'InitialLearnRate',0.005, ...
                            'LearnRateSchedule','piecewise', ...
                            'LearnRateDropPeriod',120, ...
                            'LearnRateDropFactor',0.25, ...
                            'Verbose',0);
    trainNetWork(XTrain,YTrain,layers,options,i);
end

在trainNetWork函数中添加了reshape步骤，将XTrain和YTrain调整为正确的大小，并作为trainNetwork函数的输入。这样可以保证输入大小一致，避免了reshape错误。

请尝试运行修复后的代码，并查看是否还出现reshape错误。