transformer时间序列单变量单步时间序列预测代码

以下是使用Transformer进行时间序列单变量单步预测的代码示例：

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader

# 定义Transformer模型
class TransformerModel(nn.Module):
    def __init__(self, input_dim, output_dim, d_model, nhead, num_layers, dropout):
        super(TransformerModel, self).__init__()
        self.d_model = d_model
        self.pos_encoder = PositionalEncoding(d_model, dropout)
        encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
        self.fc = nn.Linear(d_model, output_dim)

    def forward(self, src):
        src = src.permute(1, 0) # 将输入转置为(seq_len, batch_size)
        src = self.pos_encoder(src * np.sqrt(self.d_model))
        output = self.transformer_encoder(src)
        output = self.fc(output[-1, :, :]) # 取最后一个时间步的输出
        return output

# 定义位置编码层
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)

# 定义时间序列数据集类
class TimeSeriesDataset(Dataset):
    def __init__(self, data, seq_length):
        self.data = data
        self.seq_length = seq_length

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

    def __getitem__(self, idx):
        idx = idx + self.seq_length
        x = self.data[idx - self.seq_length : idx]
        y = self.data[idx]
        return x, y

# 加载数据
data = pd.read_csv('data.csv')['value'].values.astype(np.float32)
train_data = data[:1000]
test_data = data[1000:]

# 定义超参数
input_dim = 1
output_dim = 1
d_model = 32
nhead = 4
num_layers = 2
dropout = 0.2
lr = 0.001
batch_size = 32
num_epochs = 100
seq_length = 10

# 初始化模型和优化器
model = TransformerModel(input_dim, output_dim, d_model, nhead, num_layers, dropout)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
criterion = nn.MSELoss()

# 训练模型
train_dataset = TimeSeriesDataset(train_data, seq_length)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
for epoch in range(num_epochs):
    for i, (x, y) in enumerate(train_loader):
        optimizer.zero_grad()
        y_pred = model(x.unsqueeze(-1))
        loss = criterion(y_pred, y.unsqueeze(-1))
        loss.backward()
        optimizer.step()
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 测试模型
test_dataset = TimeSeriesDataset(test_data, seq_length)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
model.eval()
with torch.no_grad():
    y_pred_list = []
    for x, y in test_loader:
        y_pred_list.append(model(x.unsqueeze(-1)))
    y_pred = torch.cat(y_pred_list, dim=0)
    y_true = torch.tensor(test_data[seq_length:], dtype=torch.float32).unsqueeze(-1)
    test_loss = criterion(y_pred, y_true)
    print('Test Loss: {:.4f}'.format(test_loss.item()))

这段代码中，我们定义了`TransformerModel`类来实现Transformer模型，并定义了`PositionalEncoding`类来实现位置编码层。我们还定义了`TimeSeriesDataset`类来加载时间序列数据。

在训练模型时，我们使用`TimeSeriesDataset`类加载数据，并使用`DataLoader`类将数据分成小批量进行训练。我们使用均方误差损失函数和Adam优化器进行训练。

在测试模型时，我们使用`TimeSeriesDataset`类加载测试数据，并使用训练好的模型对测试数据进行预测。预测结果与真实值进行比较，计算测试集上的损失函数。