LSTM-Attention时间序列预测的matlab代码

以下是使用LSTM-Attention模型进行时间序列预测的MATLAB代码：

% 加载数据
data = load('data.csv');
X = data(:, 1:end-1);
Y = data(:, end);

% 划分训练集和测试集
train_ratio = 0.8;
num_train = floor(train_ratio * size(X, 1));
X_train = X(1:num_train, :);
Y_train = Y(1:num_train, :);
X_test = X(num_train+1:end, :);
Y_test = Y(num_train+1:end, :);

% 构建LSTM-Attention模型
num_features = size(X_train, 2);
num_hidden = 32;
num_attention = 16;

input_layer = sequenceInputLayer(num_features, 'Name', 'input');
lstm_layer = lstmLayer(num_hidden, 'OutputMode', 'last', 'Name', 'lstm');
attention_layer = attentionLayer(num_attention, 'Name', 'attention');
fc_layer = fullyConnectedLayer(1, 'Name', 'fc');
output_layer = regressionLayer('Name', 'output');

layers = [input_layer
          lstm_layer
          attention_layer
          fc_layer
          output_layer];

% 设置训练选项
options = trainingOptions('adam', ...
                          'MaxEpochs', 100, ...
                          'MiniBatchSize', 32, ...
                          'GradientThreshold', 1, ...
                          'InitialLearnRate', 0.001, ...
                          'LearnRateSchedule', 'piecewise', ...
                          'LearnRateDropFactor', 0.1, ...
                          'LearnRateDropPeriod', 50, ...
                          'Shuffle', 'every-epoch', ...
                          'Verbose', 1, ...
                          'Plots', 'training-progress');

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

% 预测结果
Y_pred = predict(net, X_test');
mse = mean((Y_test - Y_pred').^2);
fprintf('MSE: %.4f\n', mse);

需要注意的是，此代码中使用了MATLAB Deep Learning Toolbox中的一些函数，如sequenceInputLayer、lstmLayer、attentionLayer等。如果你没有安装这些工具箱，需要先安装并添加到MATLAB路径中。此外，代码中也需要加载数据文件data.csv，如果你的数据格式不同，需要相应地修改代码。