ARIMA模型选择与残差

%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format='retina'
import pandas as pd
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
import statsmodels.tsa.api as smt
#Display and Plotting
import matplotlib.pylab as plt
import seaborn as sns
# pandas与numpy属性设置
pd.set_option('display.float_format',lambda x:'%.5f'%x)#pandas
np.set_printoptions(precision=5,suppress=True) #numpy
pd.set_option('display.max_columns',100)
pd.set_option('display.max_rows',100)
#seaborn.plotting style
sns.set(style='ticks',context='poster')

filename_ts='series1.csv'#数据地址
ts_df=pd.read_csv(filename_ts,index_col=0,parse_dates=[0])#index_col=0设置日期为索引，时间序列模型构建的要求
n_sample=ts_df.shape[0]#获取数据的行数
print(ts_df.shape)
print(ts_df.head())

(120, 1)
value
2006-06-01 0.2150