python实现-时间序列分析

python实现-时间序列分析

import numpy as np
import pandas as pd
import datetime 
import matplotlib.pyplot as plt
import statsmodels.api as sm
from dateutil.relativedelta import relativedelta
from statsmodels.tsa.seasonal import seasonal_decompose

#首先处理下数据
data=pd.read_excel("/Users/huangqiankun/Downloads/时时序数据.xlsx",index_col=0)
print(data.head(10))
data.index.name = None  # 将index的name取消
data.reset_index(inplace=True)
data.drop(data.index[64], inplace=True)
start = datetime.datetime.strptime("2003-01", "%Y-%m")  # 把一个时间字符串解析为时间元组
date_list = [start + relativedelta(months=x*3) for x in range(0, 64)]  # 从2003-01-01开始逐月增加组成list
data['index'] = date_list
data.set_index(['index'], inplace=True)
print(data.head(10))

 传统汽车销量  国内生产总值当季值(亿元)x1    ...      汽车整车股票指数    消费者信心指数
index                                  ...                         
2003-01-01   102.1          29825.5    ...       1696.81  97.700000
2003-04-01   110.0          32537.3    ...       1912.54  87.666667
2003-07-01   112.1          35291.9    ...       1803.71  92.333333
2003-10-01   122.8          39767.4    ...       1922.48  94.666667
2004-01-01   131.1          34544.6    ...       1930.71  95.333333
2004-04-01   133.3          38700.8    ...       1245.70  92.566667
2004-07-01   121.0          41855.0    ...       1163.16  91.066667
2004-10-01   123.7          46739.8    ...        870.61  92.566667
2005-01-01   139.7          40453.3    ...        749.62  93.933333
2005-04-01   162.2          44793.1    ...        764.84  94.533333

来源：CSDN

作者：yxjwhhhh

链接：https://blog.csdn.net/yxjwhhhh/article/details/104732195