informer代码中如何设定预测的长度

### Informer 时间序列预测示例代码 以下是基于 PyTorch 的 Informer 模型实现时间序列预测的一个简单示例。此代码展示了如何构建和训练一个基本的 Informer 模型来完成多步预测。 ```python import torch import torch.nn as nn from torch.utils.data import DataLoader, Dataset import numpy as np class TimeSeriesDataset(Dataset): def __init__(self, data, seq_len=96, pred_len=24): self.seq_len = seq_len self.pred_len = pred_len self.data = data def __len__(self): return len(self.data) - self.seq_len - self.pred_len + 1 def __getitem__(self, idx): x = self.data[idx:idx+self.seq_len] y = self.data[idx+self.seq_len:idx+self.seq_len+self.pred_len] return torch.tensor(x, dtype=torch.float), torch.tensor(y, dtype=torch.float) class ProbSparseAttention(nn.Module): """概率稀疏注意力机制""" def forward(self, queries, keys, values): B, L_Q, D = queries.shape _, L_K, _ = keys.shape scores = torch.matmul(queries, keys.transpose(-2, -1)) / (D ** 0.5) top_k_scores, indices = torch.topk(scores, k=int(L_K * 0.5), dim=-1) sparse_mask = torch.zeros_like(scores).scatter_(-1, indices, top_k_scores) attention_weights = torch.softmax(sparse_mask, dim=-1) context_vector = torch.matmul(attention_weights, values) return context_vector class EncoderLayer(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.attention = ProbSparseAttention() self.linear_projection = nn.Linear(d_model, d_model) def forward(self, x): attn_output = self.attention(x, x, x) output = self.linear_projection(attn_output) return output class DecoderLayer(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.self_attention = ProbSparseAttention() self.cross_attention = ProbSparseAttention() self.linear_projection = nn.Linear(d_model, d_model) def forward(self, x, enc_output): self_attn_output = self.self_attention(x, x, x) cross_attn_output = self.cross_attention(self_attn_output, enc_output, enc_output) output = self.linear_projection(cross_attn_output) return output class InformerModel(nn.Module): def __init__(self, input_size, hidden_size, output_size, num_layers, n_heads): super(InformerModel, self).__init__() self.encoder_embedding = nn.Linear(input_size, hidden_size) self.decoder_embedding = nn.Linear(input_size, hidden_size) self.encoder_layers = nn.ModuleList([EncoderLayer(hidden_size, n_heads) for _ in range(num_layers)]) self.decoder_layers = nn.ModuleList([DecoderLayer(hidden_size, n_heads) for _ in range(num_layers)]) self.final_linear = nn.Linear(hidden_size, output_size) def forward(self, src, tgt): encoded_src = self.encoder_embedding(src.permute(0, 2, 1)).permute(0, 2, 1) decoded_tgt = self.decoder_embedding(tgt.permute(0, 2, 1)).permute(0, 2, 1) encoder_outputs = encoded_src for layer in self.encoder_layers: encoder_outputs = layer(encoder_outputs) decoder_outputs = decoded_tgt for layer in self.decoder_layers: decoder_outputs = layer(decoder_outputs, encoder_outputs) final_output = self.final_linear(decoder_outputs) return final_output.squeeze() def train(model, dataloader, criterion, optimizer, device='cpu'): model.train() total_loss = 0 for batch_x, batch_y in dataloader: batch_x, batch_y = batch_x.to(device), batch_y.to(device) outputs = model(batch_x.unsqueeze(-1), batch_y[:, :-1].unsqueeze(-1)) loss = criterion(outputs, batch_y[:, 1:].unsqueeze(-1)) optimizer.zero_grad() loss.backward() optimizer.step() total_loss += loss.item() avg_loss = total_loss / len(dataloader) return avg_loss # 数据准备 data = np.sin(np.linspace(0, 100, 1000)) # 使用正弦波作为模拟数据 dataset = TimeSeriesDataset(data=data, seq_len=96, pred_len=24) dataloader = DataLoader(dataset, batch_size=32, shuffle=True) # 初始化模型、损失函数和优化器 model = InformerModel(input_size=1, hidden_size=64, output_size=1, num_layers=2, n_heads=8) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) for epoch in range(10): # 训练10轮 loss = train(model, dataloader, criterion, optimizer) print(f'Epoch {epoch+1}, Loss: {loss:.4f}') ``` 上述代码实现了 Informer 模型的核心部分，包括编码层、解码层以及概率稀疏注意力机制[^1]。通过调整超参数（如 `seq_len` 和 `pred_len`），可以适应不同的时间序列长度需求。 #### 注意事项 - 上述代码中的 `ProbSparseAttention` 是简化版的概率稀疏注意力模块，实际应用中可能需要更复杂的实现。 - 正弦波被用作测试数据集；真实场景下应替换为具体业务的数据源。

以下是一个简单的 MATLAB 代码示例，用于使用 Informer 模型进行时间序列预测： ```matlab % 加载 Informer 模型 load('informer_model.mat'); % 定义输入序列和预测长度 input_seq = [1 2 3 4 5 6 7 8 9 10]; pred_length = 5; % 循环预测未来值 for i = 1:pred_length % 将输入序列传递给 Informer 模型 output = predict(informer_model, input_seq); % 从输出中提取下一个预测值 next_pred = output(end); % 将预测值添加到输入序列中，准备下一次预测 input_seq = [input_seq next_pred]; end % 输出预测结果 disp(input_seq(end-pred_length+1:end)); ``` 请注意，此示例代码仅用于演示目的，实际应用时需要根据实际情况进行修改和优化。此外，还需要加载适当的数据集和预处理步骤。