TCN多特征预测代码

TCN（Temporal Convolutional Network，时间卷积网络）是一种用于处理序列数据的深度学习模型，特别适用于时间序列预测任务，如股票价格、气象数据或音频信号分析。在多特征预测中，TCN可以同时考虑多个输入特征，提高预测精度。

以下是一个简单的TCN多特征预测代码示例（基于PyTorch框架），假设我们有一个包含两个特征的时间序列数据集：

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader

# 假设特征A和B的数据结构
class MultiFeatureDataset(Dataset):
    def __init__(self, data_A, data_B, target, seq_len, shift=1):
        self.data_A = data_A
        self.data_B = data_B
        self.target = target
        self.seq_len = seq_len
        self.shift = shift

    def __getitem__(self, idx):
        start_idx = idx * self.shift
        end_idx = start_idx + self.seq_len
        X = torch.stack([self.data_A[start_idx:end_idx], self.data_B[start_idx:end_idx]])
        y = self.target[idx + self.shift]  # 预测目标是未来一时间步
        return X, y

    def __len__(self):
        return len(self.target) - self.seq_len - self.shift

# 构建TCN模型
class TemporalConvNet(nn.Module):
    def __init__(self, input_channels, output_channels, kernel_size, num_layers):
        super(TemporalConvNet, self).__init__()
        self.tcn = nn.Sequential(
            nn.Conv1d(input_channels, output_channels, kernel_size, padding=(kernel_size-1)//2),
            nn.ReLU(),
            nn.BatchNorm1d(output_channels),
            nn.Dropout(0.2),
            # 添加更多相同层，num_layers-1次
            *[nn.Conv1d(output_channels, output_channels, kernel_size, padding=(kernel_size-1)//2) for _ in range(num_layers-1)],
            nn.ReLU(),
            nn.BatchNorm1d(output_channels)
        )

    def forward(self, x):
        return self.tcn(x)

# 使用模型
input_channels = 2  # 特征数量
output_channels = 64  # 卷积核数
kernel_size = 3  # 卷积窗口大小
num_layers = 3  # 层数
model = TemporalConvNet(input_channels, output_channels, kernel_size, num_layers)

# 定义优化器和损失函数
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()

# 创建数据加载器
seq_len = 10  # 序列长度
dataset = MultiFeatureDataset(data_A, data_B, target_data, seq_len)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# 训练过程
for epoch in range(num_epochs):
    for X, y in dataloader:
        pred = model(X)
        loss = criterion(pred, y.unsqueeze(-1))  # unsqueeze是为了适应模型期望的维度
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()