title: LinAlgError when modeling ARIMA
body: <p>I'm facing a weird problem when I'm modeling ARIMA and checking MSE.</p>
  
  <p>Here's code that I'm trying out.</p>
  
  <pre><code>from sklearn.metrics import mean_squared_error
  import sys
  
  split_point = int(len(value_series) * 0.66)
  train, test = value_series.values[0:split_point], value_series.values[split_point:]
  history = [float(x) for x in train]
  predictions = list()
  
  for t in range(len(test)):
      try:
          model = ARIMA(history, order=(2,1,2))
          model_fit = model.fit(disp=0)
          output = model_fit.forecast()
          yhat = output[0]
          predictions.append(yhat)
          obs = test[t]
          history.append(obs)
          print('# %s predicted=%f, expected=%f' % (t, yhat, obs))
      except:
          print("Unexpected error:", sys.exc_info()[0])
          pass
  
  error = mean_squared_error(test, predictions)
  print('Test MSE: %.3f' % error)
  </code></pre>
  
  <p>The error that I got is <code>Unexpected error: &lt;class 'numpy.linalg.linalg.LinAlgError'&gt;</code> on the line <code>model_fit = model.fit(disp=0)</code>.
  The error came out from 282th to the end of data which has the 343 length of list and still I couldn't find any solutions and causes.</p>
  
  <p>Anyway, the outputs of length of the predictions and test are 282 and 343 respectively. I don't know why predictions couldn't attach yhat, which means yhat couldn't be assigned by output from arima.fit.forcast()...</p>
  
  <p>+) And that's with <code>SVD did not converge</code> error.</p>