TCN用于时间序列预测的Matlab代码

下面是一个简单的TCN时间序列预测的MATLAB代码示例：

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

% 划分训练集和测试集
train_size = floor(size(X, 1) * 0.8);
train_X = X(1:train_size, :);
train_Y = Y(1:train_size, :);
test_X = X(train_size+1:end, :);
test_Y = Y(train_size+1:end, :);

% 网络参数
input_size = size(train_X, 2);
output_size = size(train_Y, 2);
num_channels = [16 32 64]; % 每层卷积的输出通道数
kernel_size = 3; % 卷积核大小
dropout_rate = 0.2; % dropout概率

% 定义模型
layers = [
    sequenceInputLayer(input_size)
    sequenceFoldingLayer('TimeSteps', size(train_X, 1))
    sequenceConvolutionLayer(kernel_size, num_channels(1), 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    sequenceConvolutionLayer(kernel_size, num_channels(2), 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    sequenceConvolutionLayer(kernel_size, num_channels(3), 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    sequenceUnfoldingLayer('Name', 'unfold')
    dropoutLayer(dropout_rate)
    sequenceConvolution2dLayer(1, output_size, 'Padding', 'same', 'Name', 'output')
    regressionLayer
];

% 训练模型
miniBatchSize = 32;
options = trainingOptions('adam', ...
    'MaxEpochs', 100, ...
    'MiniBatchSize', miniBatchSize, ...
    'Shuffle', 'every-epoch', ...
    'Plots', 'training-progress');
net = trainNetwork(train_X, train_Y, layers, options);

% 预测
pred_Y = predict(net, test_X, 'MiniBatchSize', miniBatchSize);

需要注意的是，这里使用了MATLAB的深度学习工具箱，包括了一些TCN的层，比如`sequenceConvolutionLayer`和`sequenceUnfoldingLayer`。如果没有安装深度学习工具箱，可以使用其他深度学习框架来实现TCN。