From 4784f76ec87779c2ef3a76c8509b9f0c302bc1ca Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 5 Oct 2020 01:03:06 +0200
Subject: [PATCH] Adjust placement of tables

---
 tex/4_stu/4_overall.tex | 14 +++++++-------
 tex/4_stu/6_fams.tex    | 26 +++++++++++++-------------
 2 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/tex/4_stu/4_overall.tex b/tex/4_stu/4_overall.tex
index dff9559..4d11718 100644
--- a/tex/4_stu/4_overall.tex
+++ b/tex/4_stu/4_overall.tex
@@ -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
diff --git a/tex/4_stu/6_fams.tex b/tex/4_stu/6_fams.tex
index 8fdd17d..c398824 100644
--- a/tex/4_stu/6_fams.tex
+++ b/tex/4_stu/6_fams.tex
@@ -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