lstm时间序列预测模型代码实现

### LSTM 时间序列预测模型实现

对于构建LSTM时间序列预测模型，可以采用PyTorch框架来完成。下面展示如何创建一个基于LSTM的时间序列预测模型。

#### 数据准备
为了适应LSTM网络的要求，需要调整输入数据的形状。通常情况下，LSTM期望接收的数据格式为 `(样本数, 时间步长, 特征数量)`[^1]。

import torch
from torch.utils.data import TensorDataset, DataLoader

def prepare_data(X, y, batch_size=4):
    # 调整X的维度以匹配LSTM输入要求 (batch_size, seq_len, input_size)
    X_tensor = torch.tensor(X).float().unsqueeze(0)  # 增加批次维度并转换成张量
    y_tensor = torch.tensor(y).float()
    
    dataset = TensorDataset(X_tensor, y_tensor)
    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

    return dataloader

#### 定义LSTM模型结构
定义一个继承自`nn.Module`类的LSTM模型，并设置好隐藏层大小、层数等超参数。

import torch.nn as nn

class LSTMModel(nn.Module):
    def __init__(self, input_dim=2, hidden_dim=50, num_layers=2, output_dim=1):
        super(LSTMModel, self).__init__()
        
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(x.device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(x.device)
        
        out, _ = self.lstm(x, (h0.detach(), c0.detach()))
        out = self.fc(out[:, -1, :])  
        return out

#### 训练过程
编写训练循环函数来进行模型训练，在此过程中会不断更新权重使得损失最小化。

device = 'cuda' if torch.cuda.is_available() else 'cpu'

model = LSTMModel().to(device)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

epochs = 100
for epoch in range(epochs):
    model.train()
    running_loss = 0
    
    for inputs, labels in train_loader:
        inputs, labels = inputs.to(device), labels.unsqueeze(-1).to(device)
        
        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
    
    print(f'Epoch [{epoch+1}/{epochs}], Loss: {running_loss/len(train_loader):.4f}')