alexander/urban-meal-delivery-demand-forecasting
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Remove glossary

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Alexander Hess 2020-11-30 18:42:54 +01:00
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tex/3_mod/5_mase.tex
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@ -41,7 +41,7 @@ These numerical instabilities occurred so often in our studies that we argue
against using such measures.
\item \textbf{Scaled Errors}:
\cite{hyndman2006} contribute this category and introduce the mean absolute
scaled error (\gls{mase}).
scaled error (MASE).
It is defined as the MAE from the actual forecasting method on the test day
(i.e., "out-of-sample") divided by the MAE from the (seasonal) na\"{i}ve
method on the entire training set (i.e., "in-sample").
@ -84,4 +84,4 @@ We conjecture that percentage error measures may be usable for UDPs facing a
higher overall demand with no intra-day down-times in between but have to
leave that to a future study.
Yet, even with high and steady demand, divide-by-zero errors are likely to
occur.
occur.