So, we need to difference the time series using a fractional value.In the world of data science there is no one superior model, the model that works on your data depends greatly on your dataset. Knowledge of various models allows us to choose one that work on our data and experimenting ...
A p-value greater than 0.05 also suggests that the time series is non-stationary.Alternatively, p-value less than or equal to 0.05, or ADF Statistic less than critical values suggest the time series is stationary.Hence, the time series we are dealing with is already stationary. In case of...
from statsmodels.tsa.seasonal import seasonal_decomposefrom statsmodels.tsa.stattools import adfuller as ADF # 计算原始序列、一阶差分序列、二阶差分序列的单位根检验结果df['diff_1'] = df['diff_1'].fillna(0)df['diff_2'] = df['diff_2'].fillna(0) timeseries_adf = ADF(df['close'].tolis...
我们先用原数据进行检验看看。 def testStationarity(ts):dftest = adfuller(ts)# 对上述函数求得的值进行语义描述dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])for key,value in dftest[4].items():dfoutput['Critical Value ...
(14,5)) # 对size个数据进行移动平均 rol_mean = timeseries.rolling(window=size).mean() # 对size个数据移动平均的方差 rol_std = timeseries.rolling(window=size).std() timeseries.plot(color='k', label='Original') rol_mean.plot(color='red', label='Rolling Mean') rol_std.plot(color='...
dftest = adfuller(timeseries, autolag='AIC') dfoutput = pd.Series(dftest[0:4], index=['Test Statistic', 'p-value', '#Lags Used', 'Number of Observations Used']) for key, value in dftest[4].items(): dfoutput['Critical Value (%s)' % key] = value ...
1### 差分运算2defdiff(timeseries):3new_df=timeseries.diff(periods=1).dropna()#dropna删除NaN4new_df.plot(color='orange',title='diff1')5returnnew_df67#进行一阶差分8ndf=diff(df['水位']) 或者如下方式也可以进行差分 ndf = pd.Series(np.diff(df['水位'], n=1)) 注:...
https://www.howtoing.com/a-guide-to-time-series-forecasting-with-arima-in-python-3/ http://www.statsmodels.org/stable/datasets/index.html """ import warnings import itertools import pandas as pd import numpy as np import statsmodels.api as sm ...
df7=pd.Series()foriinrange(180) : df3= df2[:-4*(1+i)] model = ARIMA(df3, order=(1,0,4)) result_arima1 = model.fit() df4 = df3.reset_index(drop=False) rows = df4.shape[0] endtime = df4.iloc[rows -1,0]
df7=pd.Series() for i in range(180) : df3= df2[:-4*(1+i)] model = ARIMA(df3, order=(1, 0, 4)) result_arima1 = model.fit() df4 = df3.reset_index(drop=False) rows = df4.shape[0] endtime = df4.iloc[rows - 1, 0] ...