transformer时间序列数据的代码实现

Transformer模型主要用于处理序列到序列的问题，例如自然语言翻译，但也可以应用于时间序列预测。在PyTorch中，可以使用`torch.nn.Transformer`模块来构建基于Transformer的时间序列模型。这里是一个简单的例子，展示如何实现基本的Transformer结构用于预测一个时间序列：

import torch
from torch import nn

class TransformerForTimeSeries(nn.Module):
    def __init__(self, input_dim, output_dim, num_layers=6, d_model=512, num_heads=8, dropout=0.1):
        super(TransformerForTimeSeries, self).__init__()
        self.embedding = nn.Embedding(input_dim, d_model)
        self.positional_encoding = PositionalEncoding(d_model, dropout)
        encoder_layer = nn.TransformerEncoderLayer(d_model, num_heads, dim_feedforward=2048, dropout=dropout)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers)
        self.fc_out = nn.Linear(d_model, output_dim)

    def forward(self, x):
        # x (batch_size, sequence_length)
        x = self.embedding(x) * math.sqrt(self.d_model)  # Scale the embeddings
        x = self.positional_encoding(x)
        x = self.transformer_encoder(x)
        # Average pooling over the sequence dimension to get a single prediction for each sample
        out = x.mean(dim=1)
        return self.fc_out(out)

# 初始化模型实例
transformer = TransformerForTimeSeries(input_dim, output_dim)

在这个例子中，`input_dim`是你的时间序列特征数，`output_dim`是目标变量的维度。模型首先对输入进行嵌入和添加位置编码，然后通过多个Transformer编码层进行处理，最后通过全连接层生成预测。