Created
March 20, 2018 20:34
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adfuller-test
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from statsmodels.tsa.stattools import adfuller | |
def test_stationarity(timeseries): | |
#Determing rolling statistics | |
rolmean = pd.rolling_mean(timeseries, window=96) | |
rolstd = pd.rolling_std(timeseries, window=96) | |
#I use 96 as my window because that is how many of my observations are contained in one 24-hr-period | |
#Plot rolling statistics: | |
fig = plt.figure(figsize=(12, 8)) | |
orig = plt.plot(timeseries, color='blue',label='Original') | |
mean = plt.plot(rolmean, color='red', label='Rolling Mean') | |
std = plt.plot(rolstd, color='black', label = 'Rolling Std') | |
plt.legend(loc='best') | |
plt.title('Rolling Mean & Standard Deviation') | |
plt.show() | |
#Perform Dickey-Fuller test: | |
print('Results of Dickey-Fuller Test:') | |
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 list(dftest[4].items()): | |
dfoutput['Critical Value (%s)'%key] = value | |
print(dfoutput) |
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