Tcn多变量预测程序matlab

Tcn是一种时空卷积网络，用于多变量时间序列预测，可以使用Matlab进行编程实现。以下是一个简单的Tcn多变量预测程序示例：

% 导入数据
data = load("data.mat");
X_train = data.X_train;
Y_train = data.Y_train;
X_test = data.X_test;
Y_test = data.Y_test;

% 设定模型参数
input_size = size(X_train, 2);
output_size = size(Y_train, 2);
num_filters = 128;
filter_size = 3;
num_layers = 3;
dropout_rate = 0.5;
learning_rate = 0.001;
batch_size = 128;
num_epochs = 100;

% 定义TCN模型
model = tcnModel(input_size, output_size, num_filters, filter_size, num_layers, dropout_rate);

% 设定训练选项
options = trainingOptions('adam', ...
    'InitialLearnRate', learning_rate, ...
    'MiniBatchSize', batch_size, ...
    'MaxEpochs', num_epochs, ...
    'Verbose', true, ...
    'Plots', 'training-progress');

% 训练模型
[model, trainInfo] = trainTcnModel(model, X_train, Y_train, options);

% 预测测试集结果
Y_pred = predictTcnModel(model, X_test);

% 计算测试集误差
rmse = sqrt(mean((Y_test - Y_pred).^2));
mae = mean(abs(Y_test - Y_pred));
disp("RMSE: " + rmse);
disp("MAE: " + mae);

这段代码中，首先导入数据，然后设定模型参数和训练选项。接着定义TCN模型，使用Adam优化器进行训练，并在训练过程中显示训练进度。训练完成后，使用训练好的模型对测试集进行预测，并计算测试集误差。最后输出RMSE和MAE两个评估指标。