pythonpython实现transformer对有10个变量的多元时间序列预测，用168小时预测接下来的24小时

下面是一个基本的Python实现，使用Transformer对多元时间序列进行预测：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义Transformer模型
class TransformerModel(nn.Module):
    def __init__(self, input_size, output_size, d_model, nhead, num_layers):
        super(TransformerModel, self).__init__()
        self.model_type = 'Transformer'
        self.src_mask = None
        self.pos_encoder = PositionalEncoding(d_model)
        encoder_layers = nn.TransformerEncoderLayer(d_model, nhead)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
        self.encoder = nn.Linear(input_size, d_model)
        self.decoder = nn.Linear(d_model, output_size)

    def forward(self, src):
        src = self.encoder(src)
        src = src.transpose(0, 1)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src, self.src_mask)
        output = self.decoder(output[-1])
        return output

# 定义位置编码
class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

# 定义训练函数
def train(model, train_data, optimizer, criterion, device):
    model.train()
    total_loss = 0.
    for src, tgt in train_data:
        src = src.to(device)
        tgt = tgt.to(device)
        optimizer.zero_grad()
        output = model(src)
        loss = criterion(output, tgt)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    return total_loss / len(train_data)

# 定义测试函数
def evaluate(model, test_data, criterion, device):
    model.eval()
    total_loss = 0.
    with torch.no_grad():
        for src, tgt in test_data:
            src = src.to(device)
            tgt = tgt.to(device)
            output = model(src)
            loss = criterion(output, tgt)
            total_loss += loss.item()
    return total_loss / len(test_data)

# 定义模型参数
input_size = 10
output_size = 1
d_model = 128
nhead = 4
num_layers = 4
lr = 0.001
batch_size = 32
epochs = 100

# 定义训练数据和测试数据
train_data = []
test_data = []
# TODO: 将数据切分为训练集和测试集

# 初始化模型、优化器和损失函数
model = TransformerModel(input_size, output_size, d_model, nhead, num_layers)
optimizer = optim.Adam(model.parameters(), lr=lr)
criterion = nn.MSELoss()

# 将模型和数据移到GPU上
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
train_data = [(src.to(device), tgt.to(device)) for (src, tgt) in train_data]
test_data = [(src.to(device), tgt.to(device)) for (src, tgt) in test_data]

# 训练模型
for epoch in range(1, epochs + 1):
    train_loss = train(model, train_data, optimizer, criterion, device)
    test_loss = evaluate(model, test_data, criterion, device)
    print('Epoch [{}/{}], Train Loss: {:.4f}, Test Loss: {:.4f}'.format(epoch, epochs, train_loss, test_loss))

# 进行预测
# TODO: 使用最近的168小时的数据进行预测，得到接下来的24小时的预测结果

需要注意的是，在实际使用中，还需要对数据进行一些预处理，比如归一化、缺失值处理等等。此外，还可以对模型进行更多的优化和调整，比如增加正则化项、使用更复杂的模型结构等等。