写一下EEMD+STL+XGBoost+LightGBM+ConvLSTM读取csv格式数据的可以用于多输入时间序列预测的代码

以下是一个简单的多输入时间序列预测的示例代码，其中使用了EEMD、STL、XGBoost、LightGBM和ConvLSTM等技术：

import pandas as pd
import numpy as np
from PyEMD import EEMD
from statsmodels.tsa.seasonal import STL
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
from keras.models import Sequential
from keras.layers import ConvLSTM2D, Dropout, Dense
from sklearn.model_selection import train_test_split

# 读取CSV文件并转换为数据帧
data = pd.read_csv('data.csv')

# 数据预处理
def preprocess_data(data):
    # 基于EEMD分解进行降噪
    eemd = EEMD(trials=50)
    data = np.array(data)
    emd = eemd.eemd(data)
    imfs, res = emd[:, :-1], emd[:, -1]
    data = imfs.sum(axis=0)
    # 基于STL分解进行季节性调整
    stl = STL(data)
    stl_res = stl.resid
    stl_seasonal = stl.seasonal
    # 将数据重新组合成时间序列形式
    time_series_data = pd.DataFrame({'t': data, 't-1': np.roll(data, 1), 't-2': np.roll(data, 2), 
                                     't-3': np.roll(data, 3), 't-4': np.roll(data, 4), 
                                     'seasonal': stl_seasonal})
    time_series_data = time_series_data.dropna()
    # 分离特征和目标
    X = time_series_data.drop('t', axis=1).values
    y = time_series_data['t'].values
    return X, y

X, y = preprocess_data(data)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)

# 使用XGBoost进行预测
xgb_model = XGBRegressor(n_estimators=1000, learning_rate=0.05, objective='reg:squarederror')
xgb_model.fit(X_train, y_train)
xgb_pred = xgb_model.predict(X_test)

# 使用LightGBM进行预测
lgbm_model = LGBMRegressor(n_estimators=1000, learning_rate=0.05)
lgbm_model.fit(X_train, y_train)
lgbm_pred = lgbm_model.predict(X_test)

# 使用ConvLSTM进行预测
X_train = X_train.reshape((X_train.shape[0], 1, 6, 1, 1))
X_test = X_test.reshape((X_test.shape[0], 1, 6, 1, 1))
model = Sequential()
model.add(ConvLSTM2D(filters=64, kernel_size=(1, 3), activation='relu', input_shape=(1, 6, 1, 1)))
model.add(Dropout(0.2))
model.add(ConvLSTM2D(filters=32, kernel_size=(1, 3), activation='relu', return_sequences=True))
model.add(Dropout(0.2))
model.add(ConvLSTM2D(filters=1, kernel_size=(1, 3), activation='relu', return_sequences=False))
model.add(D