给我一个transformer模型时间序列预测的代码实例

以下是一个基于Transformer模型进行时间序列预测的代码实例，使用PyTorch实现：

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

class TimeSeriesDataset(Dataset):
    def __init__(self, data, seq_len):
        self.data = data
        self.seq_len = seq_len

    def __len__(self):
        return len(self.data) - self.seq_len

    def __getitem__(self, index):
        return self.data[index:index+self.seq_len], self.data[index+self.seq_len]

class TransformerModel(nn.Module):
    def __init__(self, input_dim, output_dim, d_model, nhead, num_layers):
        super().__init__()
        self.encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead), num_layers=num_layers)
        self.decoder = nn.Linear(d_model, output_dim)

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x[:, -1, :])
        return x

def train(model, train_loader, epochs, lr):
    optimizer = optim.Adam(model.parameters(), lr=lr)
    criterion = nn.MSELoss()

    for epoch in range(epochs):
        running_loss = 0.0
        for data, target in train_loader:
            optimizer.zero_grad()
            output = model(data.unsqueeze(0))
            loss = criterion(output, target.unsqueeze(0))
            loss.backward()
            optimizer.step()
            running_loss += loss.item()

        print(f"Epoch {epoch+1} loss: {running_loss/len(train_loader)}")

def predict(model, data, seq_len):
    model.eval()
    with torch.no_grad():
        predictions = []
        for i in range(len(data)-seq_len):
            input_seq = data[i:i+seq_len].unsqueeze(0)
            output_seq = model(input_seq)
            predictions.append(output_seq.item())

    return predictions

if __name__ == '__main__':
    # Generate sample data
    data = torch.sin(torch.arange(0, 10, 0.1))
    train_data = TimeSeriesDataset(data[:80], seq_len=10)
    train_loader = DataLoader(train_data, batch_size=1, shuffle=True)

    # Define model hyperparameters
    input_dim = 1
    output_dim = 1
    d_model = 64
    nhead = 4
    num_layers = 2

    # Create and train model
    model = TransformerModel(input_dim, output_dim, d_model, nhead, num_layers)
    train(model, train_loader, epochs=50, lr=0.001)

    # Make predictions
    predictions = predict(model, data, seq_len=10)

这个代码实例演示了如何使用Transformer模型进行时间序列预测。在这个例子中，我们生成了一个正弦波形的时间序列数据，并将其分为10个数据点一组的样本。我们使用PyTorch的DataLoader和Dataset类来创建数据集和数据加载器，然后定义了一个Transformer模型，并使用均方误差损失函数和Adam优化器进行训练。最后，我们使用训练好的模型进行预测，并返回一个包含所有预测值的列表。