code: def add_new_observations(self, y, exogenous=None, **kwargs):
          
          return self.update(y, exogenous, **kwargs)
docs: Update the endog/exog samples after a model fit.
  
          After fitting your model and creating forecasts, you're going
          to need to attach new samples to the data you fit on. These are
          used to compute new forecasts (but using the same estimated
          parameters).
  
          Parameters
          ----------
          y : array-like or iterable, shape=(n_samples,)
              The time-series data to add to the endogenous samples on which the
              ``ARIMA`` estimator was previously fit. This may either be a Pandas
              ``Series`` object or a numpy array. This should be a one-
              dimensional array of finite floats.
  
          exogenous : array-like, shape=[n_obs, n_vars], optional (default=None)
              An optional 2-d array of exogenous variables. If the model was
              fit with an exogenous array of covariates, it will be required for
              updating the observed values.
  
          **kwargs : keyword args
              Any keyword args that should be passed as ``**fit_kwargs`` in the
              new model fit.