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2020-11-30 18:42:54 +01:00

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\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{rtholt},
\textit{rtses}, and
\textit{rttheta}:
Exponential smoothing without calibration and seasonal fit
\item \textit{rtets}:
ETS calibrated as described by \cite{hyndman2008b}
\item \textit{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.