Remove dots in sub-sub-sections

tex/2_lit/2_class/2_ets.tex

@@ -1,4 +1,4 @@
-\subsubsection{Na\"{i}ve Methods, Moving Averages, and Exponential Smoothing.}
+\subsubsection{Na\"{i}ve Methods, Moving Averages, and Exponential Smoothing}
 \label{ets}
 
 Simple forecasting methods are often employed as a benchmark for more

tex/2_lit/2_class/3_arima.tex

@@ -1,4 +1,4 @@
-\subsubsection{Autoregressive Integrated Moving Averages.}
+\subsubsection{Autoregressive Integrated Moving Averages}
 \label{arima}
 
 \cite{box1962}, \cite{box1968}, and more papers by the same authors in the

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

@@ -1,4 +1,4 @@
-\subsubsection{Seasonal and Trend Decomposition using Loess.}
+\subsubsection{Seasonal and Trend Decomposition using Loess}
 \label{stl}
 
 A time series $y_t$ may exhibit different types of patterns; to fully capture

tex/2_lit/3_ml/2_learning.tex

@@ -1,4 +1,4 @@
-\subsubsection{Supervised Learning.}
+\subsubsection{Supervised Learning}
 \label{learning}
 
 A conceptual difference between classical and ML methods is the format

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

@@ -1,4 +1,4 @@
-\subsubsection{Cross-Validation.}
+\subsubsection{Cross-Validation}
 \label{cv}
 
 Because ML models are trained by minimizing a loss function $L$, the

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

@@ -1,4 +1,4 @@
-\subsubsection{Random Forest Regression.}
+\subsubsection{Random Forest Regression}
 \label{rf}
 
 \cite{breiman1984} introduce the classification and regression tree

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

@@ -1,4 +1,4 @@
-\subsubsection{Support Vector Regression.}
+\subsubsection{Support Vector Regression}
 \label{svm}
 
 \cite{vapnik1963} and \cite{vapnik1964} introduce the so-called support vector