写出EEMD+STL+XGBoost+ConvLSTM的读取csv数据的多输入单输出的时序训练及预测代码

EEMD-STL-XGBoost-ConvLSTM的读取csv数据的多输入单输出的时序训练及预测代码如下：# 加载库
import pandas as pd
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import acf, pacf
from statsmodels.tsa.arima_model import ARIMA
from xgboost import XGBRegressor
from keras.layers import LSTM, ConvLSTM2D
from keras.models import Sequential# 读取csv数据
data = pd.read_csv('data.csv')# EEMD处理
from PyEMD import EEMDeemd = EEMD()data_eemd = eemd(data)# STL处理
decomposition = seasonal_decompose(data_eemd, model='additive')data_stl = decomposition.seasonal + decomposition.trend# XGBoost模型
model = XGBRegressor()model.fit(data_stl, data_eemd)# ConvLSTM模型
model = Sequential()
model.add(ConvLSTM2D(filters=32, kernel_size=(3, 3), input_shape=(1, 3, 3)))
model.add(LSTM(32))
model.add(Dense(1))model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(data_stl, data_eemd, epochs=200, batch_size=32, verbose=1)# 预测
pred = model.predict(data_stl)