基于pytorch，写LSTM深度学习基于时间序列A预测序列B的代码

Here is a simple example code for using an LSTM in PyTorch to predict sequence B based on sequence A:

import torch
import torch.nn as nn

class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
        out, (hn, cn) = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

input_size = 1
hidden_size = 128
num_layers = 2
output_size = 1

model = LSTM(input_size, hidden_size, num_layers, output_size)

In this example, `input_size` represents the number of features in sequence A, `hidden_size` represents the number of hidden units in the LSTM layer, `num_layers` represents the number of LSTM layers, and `output_size` represents the number of outputs in sequence B. The `forward` method takes an input sequence `x` and returns the predicted sequence.

You can then train the model using the input and target sequences of A and B, respectively. Here's a simple example of how you might do this:

criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(num_epochs):
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

In this example, `num_epochs` is the number of training epochs, `inputs` is a tensor representing the input sequence A, and `targets` is a tensor representing the target sequence B. The `MSELoss` function computes the mean squared error between the predicted and target sequences. The optimizer is used to update the model parameters based on the computed gradients.