title: Refit Python Statsmodel ARIMA model parameters to new data and predict
body: <p>I've stored the coefficients of intercept, AR, MA off ARIMA model of statsmodel package</p>
  
  <pre><code>x = df_sku
  x_train = x['Weekly_Volume_Sales']
  
  x_train_log = np.log(x_train)
  x_train_log[x_train_log == -np.inf] = 0
  x_train_mat = x_train_log.as_matrix()
  
  model = ARIMA(x_train_mat, order=(1,1,1))
  model_fit = model.fit(disp=0)
  res = model_fit.predict(start=1, end=137, exog=None, dynamic=False)
  print(res)
  params = model_fit.params
  </code></pre>
  
  <p>But I'm unable to find any documentation on statsmodel that lets me refit the model parameters onto a set of new data and predict N steps. </p>
  
  <p>Has anyone been able to accomplishing refitting the model and predicting out of time samples ?</p>
  
  <p>I'm trying to accomplish something similar to R:</p>
  
  <pre><code># Refit the old model with testData
  new_model &lt;- Arima(as.ts(testData.zoo), model = old_model)
  </code></pre>