title: Python- ARIMA predictions returning all NaNs
body: <p>I'm trying to follow the time series tutorial here (using my own dataset):</p>
  
  <p><a href="https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/" rel="nofollow noreferrer">https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/</a></p>
  
  <p>Surprisingly, I am able to satisfactorily successfully reach Part 7: ARIMA. In this section, I am stumbling quite a bit. All the values in the Prediction column for it are NaN.</p>
  
  <p>In the terminal, I see
  <code>a date index has been provided but it has no associated frequency information and so will be ignored when forecasting</code></p>
  
  <p>My test data set has a few date gaps for when no transactions occurred, so I fill it with
  <code>test=test.set_index('DATE').asfreq('D', fill_value=0)</code>
  . I also do the same thing with my ARIMA dataset, so the index matches the test set.</p>
  
  <p>The rest of the relevant code is as follows:</p>
  
  <pre><code>train=df[0:180]
  test=df[180:]
  SARIMA=test.copy()
  
  fit=sm.tsa.statespace.SARIMAX(train['COUNT'], order=(1,1,1), seasonal_order=(0,0,0,5)).fit()
  SARIMA['SARIMA']=fit3.predict(start=0, 
      end=93,dynamic=True)
  
  print(SARIMA) 
  print(test)
  </code></pre>
  
  <p>In the print output, the index for the test set and ARIMA set are the same. The ARIMA contains a column <code>SARIMA</code> which contains the predictions, except they are all <code>NaN</code>. What am I missing?</p>
  
  <pre><code>test
  DATE        COUNT
  2018-06-21    1
  2018-06-22    3
  ..
  2018-11-21    3
  2018-11-22    4
  
  SARIMA
  DATE        COUNT    SARIMA
  2018-06-21    1       NaN
  2018-06-22    3       NaN
  ..
  2018-11-21    3       NaN
  2018-11-22    4        NaN
  </code></pre>
  
  <p>edit:
  for some reason <code>statsmodels</code> simply cannot detect the index frequency. I've tried
  <code>SARIMA=SARIMA.set_index('DATE').asfreq('D',fill_value=0)</code>
  <code>SARIMA.index=pd.to_datetime(SARIMA.index)</code>
  <code>SARIM.index=pd.DatetimeIndex(SARIMA.index.values, freq='D')</code>
  But the warning always appears</p>
  
  <p>edit: I straight up tried to make a new dataset in Excel:</p>
  
  <pre><code>DATE       COUNT
  2018/01/01   1
  2018/01/02   2
  ..
  2018/01/10   3
  2018/01/11   4
  </code></pre>
  
  <p>created the model with the same lines above, except setting <code>enforce_stationarity</code> and <code>enforce invertibility</code> to <code>False</code>. All the predictions are still <code>NaN</code></p>
  
  <p>edit3: using the fake excel dataset, I've come 1 step closer. Passing <code>start='2018-01-01'</code> and <code>end='2018-01-21'</code> yielded predictions of all 0s, which is better than <code>NaN</code>. Can anyone make sense of these results?</p>
  
  <p>edit4: setting <code>dynamic=False</code> returned reasonable predictions. Clearly I'm no statistican</p>