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