LSTM多步预测模型使用均方根误差的matlab代码

下面是一个简单的LSTM多步预测模型使用均方根误差的Matlab代码示例：

% 导入数据
data = csvread('data.csv');

% 分割训练集和测试集
train_ratio = 0.8;
train_size = floor(train_ratio * size(data, 1));
train_data = data(1:train_size, :);
test_data = data(train_size+1:end, :);

% 准备训练数据
X_train = [];
Y_train = [];
for i = 1:size(train_data, 1)-4
    X_train = [X_train; train_data(i:i+3, :)];
    Y_train = [Y_train; train_data(i+4, :)];
end

% 准备测试数据
X_test = [];
Y_test = [];
for i = 1:size(test_data, 1)-4
    X_test = [X_test; test_data(i:i+3, :)];
    Y_test = [Y_test; test_data(i+4, :)];
end

% 定义LSTM模型
num_features = size(train_data, 2);
num_hidden_units = 128;
num_output_units = num_features;
num_time_steps = 4;
model = lstm(num_features, num_hidden_units, num_output_units, num_time_steps);

% 训练模型
num_epochs = 100;
batch_size = 32;
learning_rate = 0.001;
model = train(model, X_train, Y_train, num_epochs, batch_size, learning_rate);

% 在测试集上进行预测
Y_pred = predict(model, X_test);

% 计算均方根误差
rmse = sqrt(mean((Y_test - Y_pred).^2));
fprintf('均方根误差: %.4f\n', rmse);

其中，`data.csv`是包含时间序列数据的CSV文件。在这个示例中，我们将数据集分为80%的训练集和20%的测试集。然后，我们使用训练集来训练LSTM模型，并使用测试集来进行预测并计算均方根误差。