alexander/urban-meal-delivery-demand-forecasting
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Adjust placement of tables

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Alexander Hess 2020-10-05 01:03:06 +02:00
commit 4784f76ec8
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GPG key ID: 344EA5AB10D868E0
2 changed files with 20 additions and 20 deletions
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tex/4_stu/4_overall.tex
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@ -44,13 +44,6 @@ As the non-seasonal \textit{hses} reaches a similar accuracy as its
So, in the absence of seasonality, models that only model a trend part are
the least susceptible to the noise.
For medium demand (i.e., $10 < \text{ADD} < 25$) and training horizons up to
six weeks, the best-performing models are the same as for low demand.
For longer horizons, \textit{hets} provides the highest accuracy.
Thus, to fit a seasonal pattern, longer training horizons are needed.
While \textit{vsvr} enters the top three, \textit{hets} has the edge as they
neither require parameter tuning nor real-time data.
\begin{center}
\captionof{table}{Top-3 models by training weeks and average demand
($1~\text{km}^2$ pixel size, 60-minute time steps)}
@ -206,6 +199,13 @@ While \textit{vsvr} enters the top three, \textit{hets} has the edge as they
\end{tabular}
\end{center}
For medium demand (i.e., $10 < \text{ADD} < 25$) and training horizons up to
six weeks, the best-performing models are the same as for low demand.
For longer horizons, \textit{hets} provides the highest accuracy.
Thus, to fit a seasonal pattern, longer training horizons are needed.
While \textit{vsvr} enters the top three, \textit{hets} has the edge as they
neither require parameter tuning nor real-time data.
In summary, except for high demand, simple models trained on horizontal time
series work best.
By contrast, high demand (i.e., $25 < \text{ADD} < \infty$) and less than