LSTM多步预测模型matlab代码

以下是使用LSTM进行多步预测的MATLAB示例代码：

%加载数据
data = load('data.mat');
X = data.X;
Y = data.Y;

%将数据分为训练集和测试集
train_ratio = 0.8;
train_size = round(train_ratio * size(X,1));
X_train = X(1:train_size, :);
Y_train = Y(1:train_size, :);
X_test = X(train_size+1:end, :);
Y_test = Y(train_size+1:end, :);

%设置LSTM网络参数
num_features = size(X_train, 2);
num_hidden_units = 100;
num_output_steps = size(Y_test, 1);
num_epochs = 50;
batch_size = 64;

%定义LSTM模型
layers = [ ...
    sequenceInputLayer(num_features)
    lstmLayer(num_hidden_units,'OutputMode','sequence')
    fullyConnectedLayer(num_output_steps)
    regressionLayer];

%设置训练选项
options = trainingOptions('adam', ...
    'MaxEpochs',num_epochs, ...
    'MiniBatchSize',batch_size, ...
    'SequenceLength','longest', ...
    'GradientThreshold',1, ...
    'Shuffle','never', ...
    'Plots','training-progress');

%训练LSTM模型
net = trainNetwork(X_train', Y_train', layers, options);

%使用LSTM模型进行多步预测
X_test = X_test';
Y_pred = predict(net, X_test, 'MiniBatchSize', batch_size);
Y_pred = Y_pred';

%绘制结果
figure;
plot(1:size(Y_test,1), Y_test, 'b');
hold on;
plot(size(Y_test,1)+1:size(Y_test,1)+size(Y_pred,1), Y_pred, 'r');
title('LSTM多步预测');
xlabel('时间步');
ylabel('目标变量');
legend('测试数据', '预测值');

请注意，代码中的数据应该是已经处理好的、适合LSTM预测的格式。如果你还没有将数据处理成适合LSTM预测的格式，请先进行数据预处理，然后再使用该代码。