写一下EEMD+XGBoost+LightGBM+LSTM代码

EMD（Empirical Mode Decomposition）是一种信号处理方法，可以将非线性和非平稳信号分解成若干个本质模态函数（Intrinsic Mode Function, IMF）。

EEMD（Ensemble Empirical Mode Decomposition）是一种改进的 EMD 方法，通过对每个 IMF 添加随机噪声来增加其鲁棒性和可重复性。

XGBoost 和 LightGBM 是两种常用的梯度提升树模型，可以用于回归和分类任务。

LSTM（Long Short-Term Memory）是一种循环神经网络模型，可以用于时间序列预测和文本生成等任务。

以下是一个使用 EEMD、XGBoost、LightGBM 和 LSTM 模型进行时间序列预测的 Python 代码示例：

import numpy as np
import pandas as pd
import pyeemd
import xgboost as xgb
import lightgbm as lgb
from keras.models import Sequential
from keras.layers import LSTM, Dense

# 加载数据
data = pd.read_csv('data.csv', header=None)
data = data.values

# 定义 EEMD 函数
def eemd(data, num_ensembles):
    eemd = pyeemd.EEMD(trials=num_ensembles)
    emd = eemd.eemd(data)
    return emd

# 定义 XGBoost 函数
def xgboost_train(X_train, y_train, X_test):
    model = xgb.XGBRegressor(n_estimators=100, max_depth=6, learning_rate=0.1)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    return y_pred

# 定义 LightGBM 函数
def lightgbm_train(X_train, y_train, X_test):
    model = lgb.LGBMRegressor(n_estimators=100, max_depth=6, learning_rate=0.1)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    return y_pred

# 定义 LSTM 函数
def lstm_train(X_train, y_train, X_test):
    X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
    X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))
    model = Sequential()
    model.add(LSTM(50, input_shape=(1, X_train.shape[2])))
    model.add(Dense(1))
    model.compile(loss='mse', optimizer='adam')
    model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
    y_pred = model.predict(X_test)
    return y_pred

# 定义预测函数
def predict(data, num_ensembles):
    emd = eemd(data, num_ensembles)
    X = np.concatenate([d.reshape(-1, 1) for d in emd], axis=1)
    y_pred_xgboost = xgboost_train(X[:-1], data[num_ensembles:], X[-1:])
    y_pred_lightgbm = lightgbm_train(X[:-1], data[num_ensembles:], X[-1:])
    y_pred_lstm = lstm_train(X[:-1], data[num_ensembles:], X[-1:])
    return y_pred_xgboost, y