code: def predict(self, Xnew=None, filteronly=False, include_likelihood=True, balance=None, **kw):
          
          
          if balance is None:
              p_balance = self.balance
          else:
              p_balance = balance
              
          # Run the Kalman filter to get the state
          (m, V) = self._raw_predict(Xnew,filteronly=filteronly, p_balance=p_balance)
  
          # Add the noise variance to the state variance
          if include_likelihood:
              V += float(self.likelihood.variance)
  
          # Lower and upper bounds
          #lower = m - 2*np.sqrt(V)
          #upper = m + 2*np.sqrt(V)
  
          # Return mean and variance
          return m, V
docs: Inputs:
          ------------------
          
          balance: bool
          Whether to balance or not the model as a whole