Remove glossary

tex/2_lit/2_class/4_stl.tex

@@ -40,8 +40,7 @@ Their main advantages stem from the fact that the models calibrate themselves
 \cite{cleveland1990} introduce a seasonal and trend decomposition using a
     repeated locally weighted regression - the so-called Loess procedure - to
     smoothen the trend and seasonal components, which can be viewed as a
-    generalization of the methods above and is denoted by the acronym
-    \gls{stl}.
+    generalization of the methods above and is denoted by the acronym STL.
 In contrast to the X11, X13, and SEATS methods, the STL supports seasonalities
     of any lag $k$ that must, however, be determined with additional
     statistical tests or set with out-of-band knowledge by the forecaster

tex/2_lit/3_ml/1_intro.tex

@@ -4,8 +4,7 @@
 ML methods have been employed in all kinds of prediction tasks in recent
     years.
 In this section, we restrict ourselves to the models that performed well in
-    our study: Random Forest (\gls{rf}) and Support Vector Regression
-    (\gls{svr}).
+    our study: Random Forest (RF) and Support Vector Regression (SVR).
 RFs are in general well-suited for datasets without a priori knowledge about
     the patterns, while SVR is known to perform well on time series data, as
     shown by \cite{hansen2006} in general and \cite{bao2004} specifically for

tex/2_lit/3_ml/3_cv.tex

@@ -5,7 +5,7 @@ Because ML models are trained by minimizing a loss function $L$, the
     resulting value of $L$ underestimates the true error we see when
     predicting into the actual future by design.
 To counter that, one popular and model-agnostic approach is cross-validation
-    (\gls{cv}), as summarized, for example, by \cite{hastie2013}.
+    (CV), as summarized, for example, by \cite{hastie2013}.
 CV is a resampling technique, which ranomdly splits the samples into a
     training and a test set.
 Trained on the former, an ML model makes forecasts on the latter.

tex/2_lit/3_ml/4_rf.tex

@@ -2,7 +2,7 @@
 \label{rf}
 
 \cite{breiman1984} introduce the classification and regression tree
-    (\gls{cart}) model that is built around the idea that a single binary
+    (CART) model that is built around the idea that a single binary
     decision tree maps learned combinations of intervals of the feature
     columns to a label.
 Thus, each sample in the training set is associated with one leaf node that

tex/2_lit/3_ml/5_svm.tex

@@ -2,7 +2,7 @@
 \label{svm}
 
 \cite{vapnik1963} and \cite{vapnik1964} introduce the so-called support vector
-    machine (\gls{svm}) model, and \cite{vapnik2013} summarizes the research
+    machine (SVM) model, and \cite{vapnik2013} summarizes the research
     conducted since then.
 In its basic version, SVMs are linear classifiers, modeling a binary
     decision, that fit a hyperplane into the feature space of $\mat{X}$ to