\subsection{Generalizability of the Methology and Findings}
\label{generalizability}

Whereas forecasting applications are always data-specific, the following
    aspects generalize to UDPs with ad-hoc transportation services:
\begin{itemize}
\item \textbf{Double Seasonality}:
The double seasonality causes a periodicity $k$ too large to be modeled by
    classical models, and we adapt the STL method in the \textit{fnaive} model
    such that it "flexibly" fits a seasonal pattern changing in a non-trivial
    way over time.
\item \textbf{Order Sparsity}:
The intermittent time series resulting from gridification require simple
    methods like \textit{hsma} or \textit{trivial} that are not as susceptible
    to noise as more sophisticated ones.
\item \textbf{Unified CV}:
A CV unified around a whole day allows evaluating classical statistical and ML
    methods on the same scale.
It is agnostic of both the type of the time series and retraining.
\item \textbf{Error Measure}:
Analogous to \cite{hyndman2006}, we emphasize the importance of choosing a
    consistent error measure, and argue for increased use of MASE.
\end{itemize}