写一下EEMD+XGBoost+LightGBM+LSTM代码

这是一个比较复杂的项目，需要涉及到多个模型和技术。下面是一个简单的示例代码，供参考：

import numpy as np
import pandas as pd
from PyEMD import EEMD
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import LSTM, Dense

# 加载数据
data = pd.read_csv("data.csv")
x = data.iloc[:, 1:].values
y = data.iloc[:, 0].values

# 使用 EEMD 进行信号分解
eemd = EEMD()
eIMFs = eemd.eemd(x)
IMFs = np.transpose(eIMFs)

# 构建特征集和目标集
features = []
for i in range(len(IMFs)):
    features.append(np.mean(IMFs[i]))
    features.append(np.std(IMFs[i]))
    features.append(np.max(IMFs[i]))
    features.append(np.min(IMFs[i]))
features = np.array(features).reshape(-1, 4)
target = y[len(y)-len(IMFs):]

# 使用 XGBoost 进行预测
xgb = XGBRegressor()
xgb.fit(features, target)
xgb_pred = xgb.predict(features)
xgb_mse = mean_squared_error(target, xgb_pred)

# 使用 LightGBM 进行预测
lgbm = LGBMRegressor()
lgbm.fit(features, target)
lgbm_pred = lgbm.predict(features)
lgbm_mse = mean_squared_error(target, lgbm_pred)

# 使用 LSTM 进行预测
lstm_features = np.array(IMFs).reshape(len(IMFs), len(IMFs[0]), 1)
lstm_target = np.array(target).reshape(len(target), 1)
lstm = Sequential()
lstm.add(LSTM(units=50, return_sequences=True, input_shape=(len(IMFs[0]), 1)))
lstm.add(LSTM(units=50))
lstm.add(Dense(units=1))
lstm.compile(optimizer='adam', loss='mean_squared_error')
lstm.fit(lstm_features, lstm_target, epochs=100, batch_size=32)
lstm_pred = lstm.predict(lstm_features)
lstm_mse = mean_squared_error(lstm_target, lstm_pred)

# 输出结果
print("XGBoost MSE:", xgb_mse)
print("LightGBM MSE:", lgbm_mse)
print("LSTM MSE:", lstm_mse)

需要注意的是，这只是一个示例代码，实际应用中需要根据数据和问题进行调整和优化。