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Alexander Hess 2020-10-05 01:03:06 +02:00
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tex/4_stu/6_fams.tex
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@ -1,19 +1,6 @@
\subsection{Results by Model Families}
\label{fams}
Besides the overall results, we provide an in-depth comparison of models
within a family.
Instead of reporting the MASE per model, we rank the models holding the
training horizon fixed to make comparison easier.
Table \ref{t:hori} presents the models trained on horizontal time series.
In addition to \textit{naive}, we include \textit{fnaive} and \textit{pnaive}
already here as more competitive benchmarks.
The tables in this section report two rankings simultaneously:
The first number is the rank resulting from lumping the low and medium
clusters together, which yields almost the same rankings when analyzed
individually.
The ranks from only high demand pixels are in parentheses if they differ.
\begin{center}
\captionof{table}{Ranking of benchmark and horizontal models
($1~\text{km}^2$ pixel size, 60-minute time steps):
@ -47,6 +34,19 @@ The ranks from only high demand pixels are in parentheses if they differ.
\end{center}
\
Besides the overall results, we provide an in-depth comparison of models
within a family.
Instead of reporting the MASE per model, we rank the models holding the
training horizon fixed to make comparison easier.
Table \ref{t:hori} presents the models trained on horizontal time series.
In addition to \textit{naive}, we include \textit{fnaive} and \textit{pnaive}
already here as more competitive benchmarks.
The tables in this section report two rankings simultaneously:
The first number is the rank resulting from lumping the low and medium
clusters together, which yields almost the same rankings when analyzed
individually.
The ranks from only high demand pixels are in parentheses if they differ.
A first insight is that \textit{fnaive} is the best benchmark in all
scenarios:
Decomposing flexibly by tuning the $ns$ parameter is worth the computational