Transformer网络进行时间序列预测的Python代码

Transformer网络在时间序列预测中通常用于自然语言处理任务，比如机器翻译。但在纯的时间序列预测（如股票价格、天气预报等）中，可以将其应用于序列到序列的学习。以下是一个简单的使用PyTorch实现Transformer模型进行时间序列预测的概述：

# 导入所需库
import torch
from torch import nn

# 定义Transformer基本模块
class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.register_buffer('pe', _generate positional encoding(d_model, max_len))

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

def _generate_positional_encoding(max_len, d_model):
    # ... 实现位置编码函数 ...

class MultiHeadAttention(nn.Module):
    # ... 实现多头注意力机制 ...

class FeedForwardNetwork(nn.Module):
    # ... 实现前馈神经网络 ...

class TransformerBlock(nn.Module):
    def __init__(self, d_model, heads, dropout=0.1):
        super().__init__()
        self.attn = MultiHeadAttention(d_model, heads)
        self.ffn = FeedForwardNetwork(d_model)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, mask=None):
        # ... 实现Transformer块的前向传递 ...

# 定义Transformer模型
class TimeSeriesTransformer(nn.Module):
    def __init__(self, input_dim, output_dim, n_layers, heads, d_model, dropout):
        super(TimeSeriesTransformer, self).__init__()
        self.embedding = nn.Linear(input_dim, d_model)
        self.positional_encoding = PositionalEncoding(d_model)
        self.transformer_blocks = nn.Sequential(*[TransformerBlock(d_model, heads, dropout) for _ in range(n_layers)])
        self.fc_out = nn.Linear(d_model, output_dim)
        
    def forward(self, x, future_timesteps=1):
        # ... 进行输入转换和时间序列预测 ...

# 使用示例
model = TimeSeriesTransformer(input_dim, output_dim, n_layers, heads, d_model, dropout)
optimizer = torch.optim.Adam(model.parameters())
# ... 训练过程 ...