写一下EEMD+XGBoost+LightGBM+ConvLSTM可以用于多输入时间序列预测的代码
时间: 2023-09-05 17:05:37 浏览: 145
### 回答1:
以下是使用EEMD、XGBoost、LightGBM和ConvLSTM进行多输入时间序列预测的Python代码示例。首先,需要导入所需的Python库和模块,如下所示:
```python
import numpy as np
from PyEMD import EEMD
import xgboost as xgb
import lightgbm as lgb
from keras.models import Sequential
from keras.layers import LSTM, Dense, ConvLSTM2D
```
接下来,假设有三个输入时间序列`X1`、`X2`和`X3`,以及一个输出时间序列`y`,它们的形状分别为`(n_samples, n_timesteps, n_features)`和`(n_samples, n_timesteps, 1)`。为了使用EEMD将输入序列转换为本征模式函数(EMD)序列,可以编写以下代码:
```python
eemd = EEMD()
X1_eemd = np.zeros_like(X1)
for i in range(n_samples):
for j in range(n_features):
X1_eemd[i, :, j] = eemd(X1[i, :, j])[0]
X2_eemd = np.zeros_like(X2)
for i in range(n_samples):
for j in range(n_features):
X2_eemd[i, :, j] = eemd(X2[i, :, j])[0]
X3_eemd = np.zeros_like(X3)
for i in range(n_samples):
for j in range(n_features):
X3_eemd[i, :, j] = eemd(X3[i, :, j])[0]
```
然后,可以将转换后的EMD序列与原始输入序列一起用于训练XGBoost和LightGBM模型。例如,以下是使用XGBoost训练模型的示例代码:
```python
X_train = np.concatenate([X1, X2, X3, X1_eemd, X2_eemd, X3_eemd], axis=-1)
y_train = y[:, -1, 0]
dtrain = xgb.DMatrix(X_train, label=y_train)
param = {'max_depth': 3, 'eta': 0.1, 'objective': 'reg:squarederror'}
num_round = 100
bst = xgb.train(param, dtrain, num_round)
```
使用LightGBM的代码类似,只需要更改模型对象和参数即可。例如,以下是使用LightGBM训练模型的示例代码:
```python
X_train = np.concatenate([X1, X2, X3, X1_eemd, X2_eemd, X3_eemd], axis=-1)
y_train = y[:, -1, 0]
lgb_train = lgb.Dataset(X_train, label=y_train)
param = {'objective': 'regression', 'metric': 'mse', 'num_leaves': 31}
num_round = 100
bst = lgb.train(param, lgb_train, num_round)
```
最后,可以使用ConvLSTM模型对转换后的EMD序列进行预测。以下是使用ConvLSTM模型进行预测的示例代码:
```python
X_train_eemd = np.concatenate([X1_eemd, X2_eemd, X3_eemd], axis=-1)
y_train = y[:, -1, 0]
model
### 回答2:
EEMD是经验模态分解法(Empirical Mode Decomposition),它是一种将非线性、非平稳信号分解成多个本征模态函数(IMFs)的方法。XGBoost和LightGBM是两种基于梯度提升算法的集成学习模型,用于回归和分类任务。ConvLSTM是一种结合了卷积神经网络(CNN)和长短期记忆网络(LSTM)的深度学习模型,用于处理时间序列数据。
下面是一个简化的示例代码,展示了如何使用EEMD、XGBoost、LightGBM和ConvLSTM模型进行多输入时间序列预测:
```python
# 导入所需库
import numpy as np
from pyeemd import eemd
import xgboost as xgb
import lightgbm as lgb
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import ConvLSTM2D, Flatten, Dense
# 定义EEMD函数
def perform_eemd(signal):
eemd_output = eemd.eemd(signal)
imfs = eemd_output[:-1] # 去除残差项
return imfs
# 定义XGBoost模型
def train_xgboost(X_train, y_train):
xgb_model = xgb.XGBRegressor()
xgb_model.fit(X_train, y_train)
return xgb_model
# 定义LightGBM模型
def train_lightgbm(X_train, y_train):
lgb_model = lgb.LGBMRegressor()
lgb_model.fit(X_train, y_train)
return lgb_model
# 定义ConvLSTM模型
def train_convlstm(X_train, y_train):
model = Sequential()
model.add(ConvLSTM2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(None, height, width, channels)))
model.add(Flatten())
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, epochs=10, batch_size=32)
return model
# 加载数据
# ...
# 对输入信号进行EEMD分解
imfs = perform_eemd(data)
# 分割训练集和测试集
X_train, y_train, X_test, y_test = split_data(imfs)
# 训练XGBoost模型
xgb_model = train_xgboost(X_train, y_train)
# 使用XGBoost模型进行预测
xgb_predictions = xgb_model.predict(X_test)
# 训练LightGBM模型
lgb_model = train_lightgbm(X_train, y_train)
# 使用LightGBM模型进行预测
lgb_predictions = lgb_model.predict(X_test)
# 训练ConvLSTM模型
convlstm_model = train_convlstm(X_train, y_train)
# 使用ConvLSTM模型进行预测
convlstm_predictions = convlstm_model.predict(X_test)
# 对模型预测结果进行综合
final_predictions = (xgb_predictions + lgb_predictions + convlstm_predictions) / 3
```
以上代码仅为简化示例,实际使用时需要根据具体需求进行适当的调整和优化。