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I am trying to run Dickey-Fuller test in statsmodels in Python but getting error P Running from python 2.7 & Pandas version 0.19.2. Dataset is from Github and imported the same
enter code here
from statsmodels.tsa.stattools import adfuller
def test_stationarity(timeseries):
print 'Results of Dickey-Fuller Test:'
dftest = ts.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
print dfoutput
test_stationarity(tr)
Gives me following error :
Results of Dickey-Fuller Test:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-15-10ab4b87e558> in <module>()
----> 1 test_stationarity(tr)
<ipython-input-14-d779e1ed35b3> in test_stationarity(timeseries)
19 #Perform Dickey-Fuller test:
20 print 'Results of Dickey-Fuller Test:'
---> 21 dftest = ts.adfuller(timeseries, autolag='AIC' )
22 #dftest = adfuller(timeseries, autolag='AIC')
23 dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
C:\Users\SONY\Anaconda2\lib\site-packages\statsmodels\tsa\stattools.pyc in adfuller(x, maxlag, regression, autolag, store, regresults)
209
210 xdiff = np.diff(x)
--> 211 xdall = lagmat(xdiff[:, None], maxlag, trim='both', original='in')
212 nobs = xdall.shape[0] # pylint: disable=E1103
213
C:\Users\SONY\Anaconda2\lib\site-packages\statsmodels\tsa\tsatools.pyc in lagmat(x, maxlag, trim, original)
322 if x.ndim == 1:
323 x = x[:,None]
--> 324 nobs, nvar = x.shape
325 if original in ['ex','sep']:
326 dropidx = nvar
ValueError: too many values to unpack
222
ADF (Augmented Dickey-Fuller) test is a statistical significance test which means the test will give results in hypothesis tests with null and alternative hypotheses. As a result, we will have a p-value from which we will need to make inferences about the time series, whether it is stationary or not.
The maxlag parameter is the maximum parameter adfuller will try, but not necessarily use. If none is specified it determines the maxpar by computing [ceil(12*(n/100)^(1/4))], so that for longer data sets it assumes that higher order lags could be present (n is amount of observations here).
The augmented Dickey–Fuller (ADF) statistic, used in the test, is a negative number. The more negative it is, the stronger the rejection of the hypothesis that there is a unit root at some level of confidence.
autolag{“AIC”, “BIC”, “t-stat”, None } Method to use when automatically determining the lag length among the values 0, 1, …, maxlag. If “AIC” (default) or “BIC”, then the number of lags is chosen to minimize the corresponding information criterion. “t-stat” based choice of maxlag.
tr must be a 1d array-like, as you can see here. I don't know what is tr in your case. Assuming that you defined tr as the dataframe that contains the time serie's data, you should do something like this:
tr = tr.iloc[:,0].values
Then adfuller will be able to read the data.
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