python实现用于多步多变量时序预测的transformer模型

可以使用PyTorch实现一个用于多步多变量时序预测的Transformer模型，以下是一个简单的实现代码：

首先，需要导入所需的库和模块：

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

接下来定义一个自定义的数据集类：

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

    def __len__(self):
        return len(self.data) - self.seq_len - self.pred_len + 1

    def __getitem__(self, idx):
        x = torch.FloatTensor(self.data[idx:idx+self.seq_len])
        y = torch.FloatTensor(self.data[idx+self.seq_len:idx+self.seq_len+self.pred_len])
        return x, y

然后定义一个Transformer模型类：

class Transformer(nn.Module):
    def __init__(self, input_size, output_size, d_model, nhead, num_layers, dropout):
        super(Transformer, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.d_model = d_model
        self.nhead = nhead
        self.num_layers = num_layers
        self.dropout = dropout

        self.pos_encoder = PositionalEncoding(d_model, dropout)
        self.encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dropout)
        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers)
        self.decoder = nn.Linear(d_model, output_size)

    def forward(self, src):
        src = src.permute(1, 0, 2)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src)
        output = self.decoder(output[-1])
        return output

其中，PositionalEncoding 是一个用于位置编码的模块：

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)

最后，定义训练和测试函数：

def train(model, optimizer, criterion, train_loader, device):
    model.train()
    train_loss = 0
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
    return train_loss / len(train_loader)

def test(model, criterion, test_loader, device):
    model.eval()
    test_loss = 0
    with torch.no_grad():
        for batch_idx, (data, target) in enumerate(test_loader):
            data, target = data.to(device), target.to(device)
            output = model(data)
            loss = criterion(output, target)
            test_loss += loss.item()
    return test_loss / len(test_loader)

最后，我们可以使用上述代码来训练和测试我们的Transformer模型。