用已有的多维时间序列数据对比lstm预测模型和transformer预测模型并且可视化python代码
时间: 2024-02-17 18:00:41 浏览: 36
好的,我可以为您提供一些代码和思路来对比LSTM和Transformer模型在时间序列预测方面的表现。
首先,我们需要准备数据。假设我们有一个包含多个时间序列的数据集,每个序列都有多个维度。我们可以使用Pandas库来读取和处理数据:
```python
import pandas as pd
# 读取数据集
df = pd.read_csv('data.csv')
# 将数据集按时间步长排序
df = df.sort_values(by=['time'])
# 将数据集分为训练集和测试集
train_size = int(len(df) * 0.8)
train_df = df[:train_size]
test_df = df[train_size:]
```
接下来,我们可以定义一个函数来将数据集转换为适合LSTM和Transformer模型的格式。对于LSTM模型,我们需要将每个序列转换为多个时间步长的输入/输出序列。对于Transformer模型,我们需要将所有序列合并为一个大的输入序列,并使用位置编码来表示不同的时间步长:
```python
import numpy as np
def prepare_data_lstm(df, num_timesteps):
X = []
y = []
for i in range(num_timesteps, len(df)):
X.append(df[i-num_timesteps:i].values)
y.append(df[i].values)
X = np.array(X)
y = np.array(y)
return X, y
def prepare_data_transformer(df, num_timesteps):
X = []
for i in range(num_timesteps, len(df)):
X.append(df[i-num_timesteps:i].values)
X = np.array(X)
return X
# 定义时间步长
num_timesteps = 10
# 准备LSTM模型的训练数据
X_train_lstm, y_train_lstm = prepare_data_lstm(train_df, num_timesteps)
X_test_lstm, y_test_lstm = prepare_data_lstm(test_df, num_timesteps)
# 准备Transformer模型的训练数据
X_train_transformer = prepare_data_transformer(train_df, num_timesteps)
X_test_transformer = prepare_data_transformer(test_df, num_timesteps)
```
现在,我们可以定义LSTM和Transformer模型并训练它们。这里我们使用Keras库来定义模型和训练模型:
```python
from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout, Input
from keras.models import Model
from keras.layers import LayerNormalization
from keras.layers import MultiHeadAttention, Add, Dropout, Flatten
# 定义LSTM模型
lstm_model = Sequential()
lstm_model.add(LSTM(units=128, input_shape=(X_train_lstm.shape[1], X_train_lstm.shape[2])))
lstm_model.add(Dropout(0.2))
lstm_model.add(Dense(units=y_train_lstm.shape[1]))
lstm_model.compile(optimizer='adam', loss='mse')
# 训练LSTM模型
lstm_model.fit(X_train_lstm, y_train_lstm, epochs=50, batch_size=32, validation_split=0.1, shuffle=False)
# 定义Transformer模型
def create_transformer_model():
inputs = Input(shape=(X_train_transformer.shape[1], X_train_transformer.shape[2]))
x = MultiHeadAttention(num_heads=8, key_dim=X_train_transformer.shape[2]//8)(inputs, inputs)
x = Add()([inputs, x])
x = LayerNormalization()(x)
x = Dropout(0.2)(x)
x = Dense(X_train_transformer.shape[2], activation='relu')(x)
x = Dense(y_train_transformer.shape[2])(x)
model = Model(inputs=inputs, outputs=x)
model.compile(optimizer='adam', loss='mse')
return model
transformer_model = create_transformer_model()
# 训练Transformer模型
transformer_model.fit(X_train_transformer, y_train_transformer, epochs=50, batch_size=32, validation_split=0.1, shuffle=False)
```
训练完成之后,我们可以使用测试集来比较LSTM和Transformer模型的预测表现。这里我们可以使用Matplotlib库来可视化结果:
```python
import matplotlib.pyplot as plt
# 对比LSTM模型和Transformer模型的预测表现
lstm_predictions = lstm_model.predict(X_test_lstm)
transformer_predictions = transformer_model.predict(X_test_transformer)
# 可视化LSTM模型和Transformer模型的预测结果
plt.plot(y_test_lstm[:,0], label='true')
plt.plot(lstm_predictions[:,0], label='lstm')
plt.plot(transformer_predictions[:,0], label='transformer')
plt.legend()
plt.show()
```
这段代码将会生成一个可视化图形,其中包括了真实值、LSTM模型预测值和Transformer模型预测值。您可以通过观察图形来比较两种模型的表现。
总体而言,LSTM模型通常在时间序列预测方面表现较好,但是Transformer模型在某些情况下也可以获得更好的结果。具体哪种模型更适合取决于您的数据集和应用场景。