这是我的第449篇原创文章。

一、引言

CNN（卷积）擅长抓“局部模式”，LSTM（长短时记忆网络）擅长记住“时间上的因果和长期依赖”，Transformer（自注意力）擅长把序列里任意两个时刻相互比较、找全局相关性，而且能并行处理。

融合方式：串联
CNN → LSTM → Transformer。先提取局部特征，再用 LSTM 建长期状态，最后用 Transformer 做全局交互。

下面通过一个具体的案例，融合CNN + LSTM + Transformer进行多变量输入单变量输出单步时间序列预测，包括模型构建、训练、预测等等。

二、实现过程

2.1 数据加载

核心代码：

df = pd.read_csv('data.csv', parse_dates=["Date"], index_col=[0]) df = pd.DataFrame(df)

结果：原始数据集总数5203

2.2 数据划分

核心代码：

test_split=round(len(df)*0.20) df_for_training=df[:-test_split] df_for_testing=df[-test_split:]

训练集：4162，测试集：1041

2.3 数据归一化

核心代码：

scaler = MinMaxScaler(feature_range=(0,1)) df_for_training_scaled = scaler.fit_transform(df_for_training) df_for_testing_scaled=scaler.transform(df_for_testing)

2.4 构造时序数据集

核心代码：

train_dataset = TimeSeriesDataset(df_for_training_scaled, seq_len=30, pred_len=1) test_dataset = TimeSeriesDataset(df_for_testing_scaled, seq_len=30, pred_len=1) train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

时序训练集和测试集数组形状：

2.5 CNN_LSTM_Transformer模型

核心代码：

class CNN_LSTM_Transformer(nn.Module): def __init__(self, input_dim=5, cnn_channels=16, lstm_hidden=32, transformer_dim=32, transformer_heads=4, transformer_layers=1, pred_len=1): super().__init__() # CNN self.cnn = nn.Conv1d(in_channels=input_dim, out_channels=cnn_channels, kernel_size=3, padding=1) self.cnn_relu = nn.ReLU() # LSTM self.lstm = nn.LSTM(input_size=cnn_channels, hidden_size=lstm_hidden, batch_first=True) # Transformer Encoder encoder_layer = nn.TransformerEncoderLayer(d_model=transformer_dim, nhead=transformer_heads, batch_first=True) self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=transformer_layers) # Projection layers self.proj_lstm = nn.Linear(lstm_hidden, transformer_dim) self.pred_len = pred_len self.fc_out = nn.Linear(transformer_dim, pred_len) def forward(self, x): # x: [batch, seq_len, 1] batch_size, seq_len, _ = x.shape # CNN expects [batch, channels, seq_len] cnn_out = self.cnn_relu(self.cnn(x.transpose(1,2))) # [B, C, T] cnn_out = cnn_out.transpose(1,2) # [B, T, C] # LSTM lstm_out, _ = self.lstm(cnn_out) # [B, T, hidden] lstm_proj = self.proj_lstm(lstm_out) # [B, T, transformer_dim] # Transformer trans_out = self.transformer(lstm_proj) # [B, T, transformer_dim] # 取最后时间步输出预测 out = self.fc_out(trans_out[:, -1, :]) # [B, pred_len] return out.unsqueeze(-1) # [B, pred_len, 1]

2.6 训练模型

核心代码：

def train_model(model, dataloader, num_epochs=50, learning_rate=1e-3, device='cpu'): optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) criterion = nn.MSELoss() model.train() loss_history = [] for epoch in range(num_epochs): epoch_losses = [] for batch_data, batch_targets in dataloader: batch_data = batch_data.to(device) batch_targets = batch_targets.to(device) optimizer.zero_grad() outputs = model(batch_data) loss = criterion(outputs, batch_targets) loss.backward() optimizer.step() epoch_losses.append(loss.item()) avg_loss = np.mean(epoch_losses) loss_history.append(avg_loss) if (epoch + 1) % 10 == 0: print(f"Epoch [{epoch + 1}/{num_epochs}], Loss: {avg_loss:.4f}") return loss_history

结果：

2.7 模型测试集评估

核心代码：

def evaluate_model(model, dataloader, device='cpu'): model.eval() preds = [] trues = [] with torch.no_grad(): for batch_data, batch_targets in dataloader: batch_data = batch_data.to(device) outputs = model(batch_data) preds.append(outputs.cpu().numpy()) trues.append(batch_targets.cpu().numpy()) preds = np.concatenate(preds, axis=0).squeeze() trues = np.concatenate(trues, axis=0).squeeze() return preds, trues

2.8 结果可视化

核心代码：

def visualize_results(loss_history, preds, trues): sns.set(font_scale=1.2) plt.rc('font', family=['Times New Roman', 'Simsun'], size=12) # 图 1：训练损失曲线 # 模型在训练过程中损失的下降情况，说明模型不断优化拟合数据。 plt.plot(loss_history, marker='o', color='dodgerblue', linestyle='-', linewidth=2) plt.title("Training Loss Curve") plt.xlabel("Epoch") plt.ylabel("MSE Loss") plt.tight_layout() plt.savefig('output_image1.png', dpi=300, format='png') plt.show() # 图 2：真实值与预测值对比曲线 # 对比曲线直观展示模型预测趋势与真实数据的匹配情况，越接近表示模型效果越好。 plt.plot(trues, label="True Values", color='limegreen') plt.plot(preds, label="Predicted Values", color='crimson') plt.title("True vs. Predicted Values") plt.xlabel("Sample Index") plt.ylabel("Trend Value") plt.legend() plt.tight_layout() plt.savefig('output_image2.png', dpi=300, format='png') plt.show()

图 1：训练损失曲线

图 2：真实值与预测值对比曲线

2.9 计算误差

核心代码：

testScore1 = math.sqrt(mean_squared_error(preds_test, trues_test)) print('Test Score: %.2f RMSE' % (testScore1)) testScore2 = mean_absolute_error(preds_test, trues_test) print('Test Score: %.2f MAE' % (testScore2)) testScore3 = r2_score(preds_test, trues_test) print('Test Score: %.2f R2' % (testScore3)) testScore4 = mean_absolute_percentage_error(preds_test, trues_test) print('Test Score: %.2f MAPE' % (testScore4))

结果：

作者简介：

读研期间发表6篇SCI数据挖掘相关论文，现在某研究院从事数据算法相关科研工作，结合自身科研实践经历不定期分享关于Python、机器学习、深度学习、人工智能系列基础知识与应用案例。致力于只做原创，以最简单的方式理解和学习，关注我一起交流成长。需要数据集和源码的小伙伴可以关注底部公众号添加作者微信。