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在处理多输出回归问题时,尤其是涉及大量时间序列特征和固定特征的情况下,确实有几种方法可以尝试。以下是一些建议和方法,结合了深度学习和传统机器学习的优势,适合使用 PyTorch 框架:
方法1:混合模型(静态特征 + 时间序列特征)
你可以使用一个混合模型来分别处理静态特征和时间序列特征,然后将它们融合在一起进行预测。
import torch
import torch.nn as nn
class HybridModel(nn.Module):
def __init__(self, static_input_dim, seq_input_dim, hidden_dim, output_dim):
super(HybridModel, self).__init__()
self.static_fc = nn.Linear(static_input_dim, hidden_dim)
self.lstm = nn.LSTM(seq_input_dim, hidden_dim, batch_first=True)
self.fc = nn.Linear(hidden_dim * 2, output_dim)
def forward(self, static_input, seq_input):
static_out = torch.relu(self.static_fc(static_input))
lstm_out, _ = self.lstm(seq_input)
lstm_out = lstm_out[:, -1, :]
combined = torch.cat((static_out, lstm_out), dim=1)
output = self.fc(combined)
return output
static_input = torch.randn(32, 15)
seq_input = torch.randn(32, 10, 400)
model = HybridModel(static_input_dim=15, seq_input_dim=400, hidden_dim=128, output_dim=400)
output = model(static_input, seq_input)
print(output.shape)
方法2:多任务学习
将400个时间序列特征视为400个不同的任务,每个任务都有一个输出。可以使用共享底层特征提取器,然后为每个任务添加单独的输出层。
class MultiTaskModel(nn.Module):
def __init__(self, input_dim, hidden_dim, num_tasks):
super(MultiTaskModel, self).__init__()
self.shared_lstm = nn.LSTM(input_dim, hidden_dim, batch_first=True)
self.task_layers = nn.ModuleList([nn.Linear(hidden_dim, 1) for _ in range(num_tasks)])
def forward(self, x):
shared_out, _ = self.shared_lstm(x)
shared_out = shared_out[:, -1, :]
outputs = [task_layer(shared_out) for task_layer in self.task_layers]
return torch.cat(outputs, dim=1)
input_data = torch.randn(32, 10, 400)
model = MultiTaskModel(input_dim=400, hidden_dim=128, num_tasks=400)
output = model(input_data)
print(output.shape)
方法3:使用 CNN 提取特征
使用卷积神经网络(CNN)提取时间序列特征,然后结合固定特征进行预测。
class CNNLSTMModel(nn.Module):
def __init__(self, static_input_dim, seq_input_dim, hidden_dim, output_dim):
super(CNNLSTMModel, self).__init__()
self.static_fc = nn.Linear(static_input_dim, hidden_dim)
self.conv1 = nn.Conv1d(seq_input_dim, 128, kernel_size=3, padding=1)
self.pool = nn.MaxPool1d(kernel_size=2)
self.lstm = nn.LSTM(128, hidden_dim, batch_first=True)
self.fc = nn.Linear(hidden_dim * 2, output_dim)
def forward(self, static_input, seq_input):
static_out = torch.relu(self.static_fc(static_input))
seq_input = seq_input.permute(0, 2, 1)
conv_out = torch.relu(self.conv1(seq_input))
conv_out = self.pool(conv_out)
conv_out = conv_out.permute(0, 2, 1)
lstm_out, _ = self.lstm(conv_out)
lstm_out = lstm_out[:, -1, :]
combined = torch.cat((static_out, lstm_out), dim=1)
output = self.fc(combined)
return output
static_input = torch.randn(32, 15)
seq_input = torch.randn(32, 10, 400)
model = CNNLSTMModel(static_input_dim=15, seq_input_dim=400, hidden_dim=128, output_dim=400)
output = model(static_input, seq_input)
print(output.shape)
方法4:XGBoost 与深度学习结合
如果 XGBoost 效果较好,可以尝试将 XGBoost 与深度学习模型结合,利用 XGBoost 提取的特征作为深度学习模型的输入。
import xgboost as xgb
xgb_model = xgb.XGBRegressor()
xgb_model.fit(X_train, y_train)
xgb_features = xgb_model.apply(X_train)
class XGBDLModel(nn.Module):
def __init__(self, xgb_input_dim, hidden_dim, output_dim):
super(XGBDLModel, self).__init__()
self.fc1 = nn.Linear(xgb_input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
xgb_input = torch.tensor(xgb_features, dtype=torch.float32)
model = XGBDLModel(xgb_input_dim=xgb_input.shape[1], hidden_dim=128, output_dim=400)
output = model(xgb_input)
print(output.shape)
这些方法可以帮助你处理多输出回归问题,并结合深度学习和传统机器学习的优势。如果你有更多具体问题或需要进一步的帮助,请随时告诉我!