时序预测 | Python实现ARIMA-CNN-LSTM差分自回归移动平均模型结合卷积长短期记忆神经网络时间序列预测-CSDN博客

预测效果

基本介绍

时序预测 | Python实现ARIMA-CNN-LSTM差分自回归移动平均模型结合卷积长短期记忆神经网络时间序列预测
直接替换数据即可用 适合新手小白
附赠案例数据 可直接运行

程序设计

• 完整程序和数据下载方式私信博主回复Python实现ARIMA-CNN-LSTM差分自回归移动平均模型结合卷积长短期记忆神经网络时间序列预测。

import itertools
import sys 
import math
import numpy as np 
import pandas as pd
from numpy import concatenate
from pandas import concat, DataFrame
import matplotlib.pyplot as plt   
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from keras.layers import Dense, Flatten, RepeatVector

from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import GRU
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers import Dropout
from statsmodels.tsa.arima_model import ARIMA
import tensorflow as tf
import statsmodels.api as sm
from keras.layers import TimeDistributed

import matplotlib
import warnings
import statsmodels
from scipy import  stats

参考资料

[1] https://blog.csdn.net/article/details/126072792?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/article/details/126044265?spm=1001.2014.3001.5502