使用基于LSTM模型的pyTorch实现滚动预测函数代码

好的，以下是一个基于LSTM模型的pyTorch实现的滚动预测函数代码示例：

import torch
import torch.nn as nn

class LSTM(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(LSTM, self).__init__()
        self.hidden_dim = hidden_dim
        self.lstm = nn.LSTM(input_dim, hidden_dim)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, inputs):
        lstm_out, _ = self.lstm(inputs.view(len(inputs), 1, -1))
        predictions = self.fc(lstm_out.view(len(inputs), -1))
        return predictions[-1]

def rolling_prediction(data, window_size, input_dim, output_dim, hidden_dim, num_layers, num_epochs, learning_rate):
    model = LSTM(input_dim, hidden_dim, output_dim)
    loss_function = nn.MSELoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

    for i in range(num_epochs):
        for j in range(len(data)-window_size):
            optimizer.zero_grad()
            model.hidden = (torch.zeros(num_layers, 1, model.hidden_dim), torch.zeros(num_layers, 1, model.hidden_dim))

            inputs = torch.tensor(data[j:j+window_size, :-output_dim], dtype=torch.float32)
            target = torch.tensor(data[j+window_size-1, -output_dim:], dtype=torch.float32)

            output = model(inputs)
            loss = loss_function(output, target)
            loss.backward()
            optimizer.step()

    predictions = []
    for j in range(len(data) - window_size):
        model.hidden = (torch.zeros(num_layers, 1, model.hidden_dim), torch.zeros(num_layers, 1, model.hidden_dim))

        inputs = torch.tensor(data[j:j+window_size, :-output_dim], dtype=torch.float32)
        output = model(inputs).detach().numpy()
        predictions.append(output)

    return predictions

该函数的输入参数包括：数据集 `data`，滑动窗口大小 `window_size`，输入维度 `input_dim`，输出维度 `output_dim`，隐藏层维度 `hidden_dim`，LSTM层数 `num_layers`，训练轮数 `num_epochs`，学习率 `learning_rate`。

该函数的输出是一个预测结果列表 `predictions`，其中每个元素是一个预测值的向量。