我想用7个特征预测一个变量，时间步长为4：

# Shape X_train: torch.Size([24433, 4, 7]
# Shape Y_train: torch.Size([24433, 4, 1]

# Shape X_test: torch.Size([6109, 4, 7]
# Shape Y_test: torch.Size([6109, 4, 1]

train_dataset = TensorDataset(X_train, Y_train)
test_dataset = TensorDataset(X_test, Y_test) 

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False)

• • 我的（初始）LSTM模型：**

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.linear = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        x, _ = self.lstm(x)
        x = self.linear(x)
        return x

model = LSTMModel(input_size=7, hidden_size=256, output_size=1)

loss_fn = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.1)

• • 适用型号：**

# Loop over the training set
for X, Y in train_loader:

    optimizer.zero_grad()
    
    Y_pred = model(X)

    loss = loss_fn(Y_pred, Y)
    
    loss.backward()
    
    optimizer.step()

model.eval()

# Loop over the test set
for X, Y in test_loader:

    Y_pred = model(X)
    
    loss = loss_fn(Y_pred, Y)

• • Y（真实数据）示例：**

tensor([[[59.],
         [59.],
         [59.],
         [59.]],

        [[70.],
         [70.],
         [70.],
         [70.]],

        [[ 100.],
         [ 0.],
         [ 0.],
         [ 0.]],

# etc.

• • 但是，我的Y_pred有点像这样：**

tensor([[[15.8224],
         [15.8224],
         [15.8224],
         [15.8224]],

        [[16.1654],
         [16.1654],
         [16.1654],
         [16.1654]],

        [[16.2127],
         [16.2127],
         [16.2127],
         [16.2127]],

# etc.

• • 我尝试过无数种方法**
• 更改模型架构（不同批量、不同层数）
• 添加压差和衰减参数
• 在训练和测试数据上循环时使用历元并更改历元数
• 具有不同学习率的不同优化器（Adam、SGD）
• 对输入数据进行日志转换

Examples of my data in a previous question.
我是PyTorch和LSTM的新手，所以我可能会做错，但是，无论我做什么改变，我总是从预测中得到一个（接近）常量的值。我做错了什么/我应该做什么？