Remove glossary

tex/3_mod/5_mase.tex

@@ -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.

tex/3_mod/7_models/2_hori.tex

@@ -15,21 +15,21 @@ As the models in this family do not include the test day's demand data in
 The models in this family are as follows; we use prefixes, such as "h" here,
     when methods are applied in other families as well:
 \begin{enumerate}
-\item \textit{\gls{naive}}:
+\item \textit{naive}:
           Observation from the same time step one week prior
-\item \textit{\gls{trivial}}:
+\item \textit{trivial}:
           Predict $0$ for all time steps
-\item \textit{\gls{hcroston}}:
+\item \textit{hcroston}:
           Intermittent demand method introduced by \cite{croston1972}
-\item \textit{\gls{hholt}},
-      \textit{\gls{hhwinters}},
-      \textit{\gls{hses}},
-      \textit{\gls{hsma}}, and
-      \textit{\gls{htheta}}:
+\item \textit{hholt},
+      \textit{hhwinters},
+      \textit{hses},
+      \textit{hsma}, and
+      \textit{htheta}:
           Exponential smoothing without calibration
-\item \textit{\gls{hets}}:
+\item \textit{hets}:
           ETS calibrated as described by \cite{hyndman2008b}
-\item \textit{\gls{harima}}:
+\item \textit{harima}:
           ARIMA calibrated as described by \cite{hyndman2008a}
 \end{enumerate}
 \textit{naive} and \textit{trivial} provide an absolute benchmark for the

tex/3_mod/7_models/3_vert.tex

@@ -16,17 +16,17 @@ By decomposing the raw time series, all long-term patterns are assumed to be
     a potential trend and auto-correlations.
 The models in this family are:
 \begin{enumerate}
-\item \textit{\gls{fnaive}},
-      \textit{\gls{pnaive}}:
+\item \textit{fnaive},
+      \textit{pnaive}:
           Sum of STL's trend and seasonal components' na\"{i}ve forecasts
-\item \textit{\gls{vholt}},
-      \textit{\gls{vses}}, and
-      \textit{\gls{vtheta}}:
+\item \textit{vholt},
+      \textit{vses}, and
+      \textit{vtheta}:
           Exponential smoothing without calibration and seasonal
                        fit
-\item \textit{\gls{vets}}:
+\item \textit{vets}:
           ETS calibrated as described by \cite{hyndman2008b}
-\item \textit{\gls{varima}}:
+\item \textit{varima}:
           ARIMA calibrated as described by \cite{hyndman2008a}
 \end{enumerate}
 As mentioned in Sub-section \ref{unified_cv}, we include the sum of the

tex/3_mod/7_models/4_rt.tex

@@ -8,13 +8,13 @@ Instead of obtaining an $H$-step-ahead forecast, we retrain a model after
     every time step and only predict one step.
 The remainder is as in the previous sub-section, and the models are:
 \begin{enumerate}
-\item \textit{\gls{rtholt}},
-      \textit{\gls{rtses}}, and
-      \textit{\gls{rttheta}}:
+\item \textit{rtholt},
+      \textit{rtses}, and
+      \textit{rttheta}:
           Exponential smoothing without calibration and seasonal fit
-\item \textit{\gls{rtets}}:
+\item \textit{rtets}:
           ETS calibrated as described by \cite{hyndman2008b}
-\item \textit{\gls{rtarima}}:
+\item \textit{rtarima}:
           ARIMA calibrated as described by \cite{hyndman2008a}
 \end{enumerate}
 Retraining \textit{fnaive} and \textit{pnaive} did not increase accuracy, and

tex/3_mod/7_models/5_ml.tex

@@ -10,7 +10,7 @@ Based on the seasonally-adjusted time series $a_t$, we employ the feature
 The ML models are trained once before a test day starts.
 For training, the matrix and vector are populated such that $y_T$ is set to
     the last time step of the day before the forecasts, $a_T$.
-As the splitting during CV is done with whole days, the \gls{ml} models are
+As the splitting during CV is done with whole days, the ML models are
     trained with training sets consisting of samples from all times of a day
     in an equal manner.
 Thus, the ML models learn to predict each time of the day.
@@ -20,8 +20,8 @@ For prediction on a test day, the $H$ observations preceding the time
 As a result, real-time data are included.
 The models in this family are:
 \begin{enumerate}
-\item \textit{\gls{vrfr}}: RF trained on the matrix as described
-\item \textit{\gls{vsvr}}: SVR trained on the matrix as described
+\item \textit{vrfr}: RF trained on the matrix as described
+\item \textit{vsvr}: SVR trained on the matrix as described
 \end{enumerate}
 We tried other ML models such as gradient boosting machines but found
     only RFs and SVRs to perform well in our study.