\subsubsection{Vertical and Real-time Forecasts with Retraining}
\label{rt}

The lower-left in Figure \ref{f:inputs} shows how models trained on vertical
    time series are extended with real-time order data as it becomes available
    during a test day:
Instead of obtaining an $H$-step-ahead forecast, we retrain a model after
    every time step and only predict one step.
The remainder is as in the previous sub-section, and the models are:
\begin{enumerate}
\item \textit{\gls{rtholt}},
      \textit{\gls{rtses}}, and
      \textit{\gls{rttheta}}:
          Exponential smoothing without calibration and seasonal fit
\item \textit{\gls{rtets}}:
          ETS calibrated as described by \cite{hyndman2008b}
\item \textit{\gls{rtarima}}:
          ARIMA calibrated as described by \cite{hyndman2008a}
\end{enumerate}
Retraining \textit{fnaive} and \textit{pnaive} did not increase accuracy, and
    thus we left them out.
A downside of this family is the significant increase in computing costs.