Merge branch 'initial-setup' into develop

.gitignore

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+*.acn
+*.aux
+*.bbl
+*.blg
+*.glo
+*.ist
+*.log
+*.spl
+*.synctex.gz

paper.tex

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+\documentclass[preprint,review,12pt]{static/elsarticle}
+
+\input{tex/preamble}
+
+\begin{document}
+
+\input{tex/meta}
+\newpage
+
+\input{tex/1_intro}
+\input{tex/2_lit/1_intro}
+\input{tex/3_mod/1_intro}
+\input{tex/4_stu/1_intro}
+\input{tex/5_con/1_intro}
+
+\newpage
+
+\input{tex/glossary}
+\newpage
+
+\appendix
+\newpage
+
+\bibliographystyle{static/elsarticle-harv}
+\bibliography{tex/references}
+
+\end{document}

tex/1_intro.tex

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+\section{Introduction}
+\label{intro}

tex/2_lit/1_intro.tex

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+\section{Literature Review}
+\label{lit}

tex/3_mod/1_intro.tex

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+\section{Model Formulation}
+\label{mod}

tex/4_stu/1_intro.tex

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+\section{Empirical Study: A Meal Delivery Platform in Europe}
+\label{stu}

tex/5_con/1_intro.tex

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+\section{Conclusion}
+\label{con}

tex/glossary.tex

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+\printglossaries

tex/meta.tex

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+\begin{frontmatter}
+
+\journal{Transportation Research Part E}
+\title{Real-time Demand Forecasting for an Urban Delivery Platform}
+
+\author[WHU]{Alexander Hess\fnref{emails}}
+\author[WHU]{Stefan Spinler\fnref{emails}}
+\author[MIT]{Matthias Winkenbach\fnref{emails}}
+\address[WHU]{
+WHU - Otto Beisheim School of Management,
+Burgplatz 2, 56179 Vallendar, Germany
+}
+\address[MIT]{
+Massachusetts Institute of Technology,
+77 Massachusetts Avenue, Cambridge, MA 02139, United States
+}
+\fntext[email]{
+Emails:
+    alexander.hess@whu.edu,
+    stefan.spinler@whu.edu,
+    mwinkenb@mit.edu
+}
+
+\begin{abstract}
+Meal delivery platforms like Uber Eats shape the landscape in cities around the world.
+This paper addresses forecasting demand into the short-term future.
+We propose an approach incorporating
+    both classical forecasting
+    and machine learning methods.
+Model evaluation and selection is adapted to demand typical for such a platform
+    (i.e., intermittent with a double-seasonal pattern).
+The results of an empirical study with a European meal delivery service show
+    that machine learning models become competitive
+    once the average daily demand passes a threshold.
+As a main contribution, the paper explains
+    how a forecasting system must be set up
+    to enable predictive routing.
+\end{abstract}
+
+\begin{keyword}
+demand forecasting \sep
+intermittent demand \sep
+machine learning \sep
+urban delivery platform
+\end{keyword}
+
+\end{frontmatter}

tex/preamble.tex

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+\usepackage[acronym]{glossaries}
+\makeglossaries