### CrossFormer 时间序列预测实现代码示例 CrossFormer 是一种利用跨维度依赖关系进行多变量时间序列预测的Transformer模型[^1]。下面提供了一个简化版本的 Python 代码示例来展示如何构建并训练一个基本的 CrossFormer 模型。 ```python import torch from torch import nn import numpy as np class PatchEmbedding(nn.Module): """Patch Embedding Layer""" def __init__(self, patch_len=16, d_model=512, dropout=0.1): super(PatchEmbedding, self).__init__() self.patch_len = patch_len # Linear transformation to map patches into embedding space. self.linear_proj = nn.Linear(patch_len, d_model) self.dropout = nn.Dropout(dropout) def forward(self, x): """ Args: x (Tensor): Input tensor of shape [batch_size, channels, length]. Returns: Tensor: Output tensor after applying the linear projection and dropout. """ batch_size, channels, seq_length = x.shape num_patches = int(seq_length / self.patch_len) # Reshape input data from [B,C,L] -> [B,N,P], where N is number of patches, # P is the size per patch. patched_x = x.unfold(dimension=-1, size=self.patch_len, step=self.patch_len).transpose(-2,-3) out = self.linear_proj(patched_x.reshape(batch_size*num_patches, -1)) out = self.dropout(out.view(batch_size, num_patches, -1)) return out class CrossAttentionLayer(nn.Module): """Single layer implementing cross attention mechanism.""" def __init__(self, embed_dim, n_heads): super(CrossAttentionLayer, self).__init__() self.attn = nn.MultiheadAttention(embed_dim, n_heads) def forward(self, query, key_value_input): attn_output, _ = self.attn(query=query, key=key_value_input, value=key_value_input) return attn_output def create_crossformer(): model = nn.Sequential( PatchEmbedding(), CrossAttentionLayer(embed_dim=512, n_heads=8), nn.TransformerEncoderLayer(d_model=512, nhead=8), nn.Flatten(start_dim=1), nn.Linear(in_features=512*int((seq_len-patch_len)/stride)+patch_len, out_features=output_size) ) return model if __name__ == "__main__": device = 'cuda' if torch.cuda.is_available() else 'cpu' # Define parameters here based on your dataset characteristics. channel_num = 1 # Number of features or variables in time series. seq_len = 96 # Length of each sequence used for prediction. stride = 1 # Stride when creating patches. output_size = 1 # Size of forecast horizon. dummy_data = torch.randn([batch_size, channel_num, seq_len]).to(device) net = create_crossformer().to(device) predictions = net(dummy_data) ``` 此代码展示了如何创建一个简单的 PyTorch 版本的 CrossFormer 架构，其中包括了补丁嵌入层和交叉注意机制。为了适应特定的应用场景，可能还需要调整超参数以及添加额外的功能模块，比如位置编码器、残差连接等。