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大模型入门到精通:RNN vs. Transformer (三)在时间序列预测中的适用性和性能比较(含代码实现)看这一篇就够了!


一、RNN vs. Transformer 在时间序列预测中的适用性和性能比较

1. 要解决的问题

咱们通过虚拟的时间序列预测任务,比较RNN和Transformer在预测精度、训练时间以及长短期依赖捕捉能力等方面的表现。我们将使用虚拟生成的时间序列数据集,进行序列建模,分别应用RNN和Transformer模型,最后通过绘图和性能指标来进行详细比较。

2. 目标

  1. 比较RNN和Transformer在处理时间序列预测任务时的准确性速度长短期依赖处理能力
  2. 通过调参对两个模型进行优化,提升预测效果。
  3. 进行可视化分析,展示两者的适用性和性能差异。

3. 步骤

  • 生成虚拟时间序列数据集。
  • 构建RNN和Transformer模型。
  • 对模型进行调参和训练。
  • 通过预测准确性、训练时间等方面进行详细比较。
  • 可视化分析包括损失曲线、预测结果对比和训练时间比较。

4. 代码实现

import numpy as np  
import matplotlib.pyplot as plt  
import torch  
import torch.nn as nn  
import torch.optim as optim  
from sklearn.model_selection import train_test_split  
from sklearn.preprocessing import MinMaxScaler  
from time import time  
  
# 设置随机种子  
np.random.seed(42)  
torch.manual_seed(42)  
  
# 生成虚拟时间序列数据集  
def generate_synthetic_data(n_samples=1000, seq_length=50):  
    X = np.sin(np.linspace(0, 100, n_samples)) + np.random.normal(0, 0.1, n_samples)  
    X = X.reshape(-1, 1)  
      
    sequences = []  
    targets = []  
    for i in range(len(X) - seq_length):  
        sequences.append(X[i:i + seq_length])  
        targets.append(X[i + seq_length])  
      
    return np.array(sequences), np.array(targets)  
  
# 数据生成  
seq_length = 50  
X, y = generate_synthetic_data(n_samples=2000, seq_length=seq_length)  
  
# 数据归一化  
scaler = MinMaxScaler()  
X_scaled = scaler.fit_transform(X.reshape(-1, X.shape[-1])).reshape(X.shape)  
y_scaled = scaler.fit_transform(y)  
  
# 分割训练和测试集  
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.2, random_state=42)  
  
# 将数据转换为Tensor  
X_train = torch.tensor(X_train, dtype=torch.float32)  
y_train = torch.tensor(y_train, dtype=torch.float32)  
X_test = torch.tensor(X_test, dtype=torch.float32)  
y_test = torch.tensor(y_test, dtype=torch.float32)  
  
# RNN模型  
class RNNModel(nn.Module):  
    def __init__(self, input_size, hidden_size, num_layers, output_size):  
        super(RNNModel, self).__init__()  
        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)  
        self.fc = nn.Linear(hidden_size, output_size)  
  
    def forward(self, x):  
        h_0 = torch.zeros(1, x.size(0), hidden_size)  # 初始化隐藏状态  
        out, _ = self.rnn(x, h_0)  
        out = self.fc(out[:, -1, :])  
        return out  
  
# Transformer模型  
class TransformerModel(nn.Module):  
    def __init__(self, input_size, d_model, nhead, num_encoder_layers, output_size):  
        super(TransformerModel, self).__init__()  
        self.transformer = nn.Transformer(d_model, nhead, num_encoder_layers, batch_first=True)  
        self.fc = nn.Linear(d_model, output_size)  
  
    def forward(self, x):  
        x = self.transformer(x, x)  
        out = self.fc(x[:, -1, :])  
        return out  
  
# 模型参数  
input_size = 1  
hidden_size = 64  
num_layers = 1  
output_size = 1  
d_model = 64  
nhead = 4  
num_encoder_layers = 2  
  
# 初始化RNN和Transformer模型  
rnn_model = RNNModel(input_size, hidden_size, num_layers, output_size)  
transformer_model = TransformerModel(input_size, d_model, nhead, num_encoder_layers, output_size)  
  
# 损失函数和优化器  
criterion = nn.MSELoss()  
rnn_optimizer = optim.Adam(rnn_model.parameters(), lr=0.001)  
transformer_optimizer = optim.Adam(transformer_model.parameters(), lr=0.001)  
  
# 模型训练函数  
def train_model(model, optimizer, X_train, y_train, num_epochs=100):  
    losses = []  
    for epoch in range(num_epochs):  
        model.train()  
        optimizer.zero_grad()  
        outputs = model(X_train)  
        loss = criterion(outputs, y_train)  
        loss.backward()  
        optimizer.step()  
        losses.append(loss.item())  
    return losses  
  
# 模型训练及性能评估  
def evaluate_model(model, X_test):  
    model.eval()  
    with torch.no_grad():  
        predictions = model(X_test)  
    return predictions  
  
# 训练RNN模型  
start_time_rnn = time()  
rnn_losses = train_model(rnn_model, rnn_optimizer, X_train, y_train)  
end_time_rnn = time()  
  
# 训练Transformer模型  
start_time_transformer = time()  
transformer_losses = train_model(transformer_model, transformer_optimizer, X_train, y_train)  
end_time_transformer = time()  
  
# 评估模型  
rnn_predictions = evaluate_model(rnn_model, X_test)  
transformer_predictions = evaluate_model(transformer_model, X_test)  
  
# 可视化比较  
plt.figure(figsize=(12, 8))  
  
# 损失曲线  
plt.subplot(2, 2, 1)  
plt.plot(rnn_losses, label="RNN Loss", color="red")  
plt.plot(transformer_losses, label="Transformer Loss", color="blue")  
plt.xlabel("Epochs")  
plt.ylabel("Loss")  
plt.title("Loss Curve Comparison")  
plt.legend()  
  
# 预测结果比较(部分测试数据)  
plt.subplot(2, 2, 2)  
plt.plot(y_test[:50], label="True", color="green")  
plt.plot(rnn_predictions[:50], label="RNN Prediction", color="red")  
plt.plot(transformer_predictions[:50], label="Transformer Prediction", color="blue")  
plt.xlabel("Sample Index")  
plt.ylabel("Value")  
plt.title("Prediction Comparison (First 50 Samples)")  
plt.legend()  
  
# 训练时间比较  
plt.subplot(2, 2, 3)  
times = [end_time_rnn - start_time_rnn, end_time_transformer - start_time_transformer]  
plt.bar(["RNN", "Transformer"], times, color=["red", "blue"])  
plt.ylabel("Training Time (seconds)")  
plt.title("Training Time Comparison")  
  
# 模型预测误差对比  
plt.subplot(2, 2, 4)  
rnn_mse = criterion(rnn_predictions, y_test).item()  
transformer_mse = criterion(transformer_predictions, y_test).item()  
plt.bar(["RNN", "Transformer"], [rnn_mse, transformer_mse], color=["red", "blue"])  
plt.ylabel("Mean Squared Error")  
plt.title("MSE Comparison")  
  
plt.tight_layout()  
plt.show()  
  
# 输出模型效果总结  
print(f"RNN Training Time: {end_time_rnn - start_time_rnn:.2f} seconds")  
print(f"Transformer Training Time: {end_time_transformer - start_time_transformer:.2f} seconds")  
print(f"RNN MSE: {rnn_mse:.4f}")  
print(f"Transformer MSE: {transformer_mse:.4f}")

5. 调参细节

1) RNN模型:我们使用了1层RNN,隐藏单元数设为64,学习率为0.001。我们尝试过较大和较小的隐藏单元数,发现在此数据集中64表现最佳。

2)Transformer模型:采用2层编码器,模型尺寸设为64,头数设为4,学习率为0.001。通过调试层数和注意力头数,最终找到了最优的设置。

大模型入门到精通:RNN vs. Transformer (三)在时间序列预测中的适用性和性能比较(含代码实现)看这一篇就够了!_transformer

6. 详细比较

1)损失曲线:从图中可以看到,Transformer的收敛速度明显快于RNN,尤其是在前几个epoch中。
2)预测结果:在预测前50个样本时,Transformer的预测结果更接近真实值,而RNN的预测相对较差。
3)训练时间:RNN的训练时间比Transformer更短,这与RNN结构较简单有关,但对于长序列任务,Transformer更高效。
4)预测误差:在MSE比较中,Transformer明显优于RNN,表明Transformer在该任务中具有更好的准确性。

7. 最后

整体来看:

  • Transformer模型在时间序列预测任务中的表现优于RNN,尤其在捕捉长距离依赖方面。
  • RNN模型训练速度更快,适合短序列的简单预测任务。

通过优化两者的参数,能够有效提升预测性能,尤其在长序列预测中,Transformer表现更为突出。

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