transformer 多元时间序列的代码

以下是使用Transformer处理多元时间序列的Python代码示例：

import torch
import torch.nn as nn

class TransformerModel(nn.Module):
    def __init__(self, input_dim, output_dim, d_model=128, num_heads=8, num_layers=6, dropout=0.1):
        super(TransformerModel, self).__init__()

        # Multi-head attention layers
        self.attention_layers = nn.ModuleList([
            nn.MultiheadAttention(d_model=d_model, num_heads=num_heads, dropout=dropout)
            for _ in range(num_layers)
        ])

        # Feedforward layers
        self.feedforward_layers = nn.ModuleList([
            nn.Sequential(
                nn.Linear(d_model, 4 * d_model),
                nn.ReLU(),
                nn.Linear(4 * d_model, d_model),
                nn.Dropout(dropout)
            )
            for _ in range(num_layers)
        ])

        # Input embedding layer
        self.input_embedding = nn.Linear(input_dim, d_model)

        # Output linear layer
        self.output_layer = nn.Linear(d_model, output_dim)

    def forward(self, x):
        # x.shape: (seq_len, batch_size, input_dim)

        # Input embedding
        x = self.input_embedding(x)
        # x.shape: (seq_len, batch_size, d_model)

        # Transpose sequence to (batch_size, seq_len, d_model)
        x = x.transpose(0, 1)
        # x.shape: (batch_size, seq_len, d_model)

        # Multi-head attention layers
        for attention_layer in self.attention_layers:
            x, _ = attention_layer(x, x, x)
            # x.shape: (batch_size, seq_len, d_model)

        # Feedforward layers
        for feedforward_layer in self.feedforward_layers:
            x = feedforward_layer(x)
            # x.shape: (batch_size, seq_len, d_model)

        # Transpose sequence back to (seq_len, batch_size, d_model)
        x = x.transpose(0, 1)
        # x.shape: (seq_len, batch_size, d_model)

        # Output linear layer
        x = self.output_layer(x)
        # x.shape: (seq_len, batch_size, output_dim)

        # Transpose sequence back to (batch_size, seq_len, output_dim)
        x = x.transpose(0, 1)
        # x.shape: (batch_size, seq_len, output_dim)

        return x

在这个模型中，我们使用了Transformer的核心组件：多头注意力层和前馈网络。输入数据的形状为(seq_len, batch_size, input_dim)，其中seq_len是时间序列的长度，batch_size是批次大小，input_dim是每个时间步的特征数。输出数据的形状为(batch_size, seq_len, output_dim)，其中output_dim是预测的特征数。

在forward方法中，我们首先将输入数据进行线性变换，将其映射到维度为d_model的向量空间中。然后通过多头注意力层和前馈网络进行特征提取。最后将输出进行线性变换，得到预测结果。

在实际使用中，我们可以根据具体的任务需求来修改模型的参数和结构，例如修改d_model、num_heads、num_layers等参数，或添加更多的线性层或卷积层来进一步提取特征。