LSTM进行均方根误差多步预测的MATLAB代码

下面是使用LSTM进行均方根误差多步预测的MATLAB代码示例：

% 加载数据
load data.mat

% 数据处理
numTimeStepsTrain = floor(0.9*numel(data));
XTrain = data(1:numTimeStepsTrain+1);
YTrain = data(2:numTimeStepsTrain+2);
XTest = data(numTimeStepsTrain+1:end-1);
YTest = data(numTimeStepsTrain+2:end);

% 定义模型参数
numFeatures = 1;
numResponses = 1;
numHiddenUnits = 200;

layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits,'OutputMode','sequence')
    fullyConnectedLayer(numResponses)
    regressionLayer];

% 定义训练参数
options = trainingOptions('adam', ...
    'MaxEpochs',100, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.005, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropFactor',0.2, ...
    'LearnRateDropPeriod',20, ...
    'Verbose',0, ...
    'Plots','training-progress');

% 训练模型
net = trainNetwork(XTrain,YTrain,layers,options);

% 预测测试集
YPred = predict(net,XTest);

% 计算均方根误差
rmse = sqrt(mean((YPred-YTest).^2));

% 绘制结果图
figure
plot(data(1:end-1))
hold on
idx = numTimeStepsTrain+1:(numTimeStepsTrain+numel(YTest));
plot(idx,[data(numTimeStepsTrain) YPred],'-.')
hold off
xlim([0,numel(data)])
xlabel("Month")
ylabel("Cases")
title("Forecast")
legend(["Observed" "Forecast"])

其中，`data.mat`是输入的时间序列数据，`numTimeStepsTrain`是训练集的时间步数，`XTrain`和`YTrain`是训练集的输入和输出数据，`XTest`和`YTest`是测试集的输入和输出数据。`numHiddenUnits`是LSTM层的隐藏单元数，`layers`定义了模型的层数和结构，`options`定义了训练的参数。`trainNetwork`函数用于训练模型，`predict`函数用于预测测试集数据，`rmse`计算了均方根误差，最后使用`plot`函数绘制了预测结果图。