diff --git a/tex/meta.tex b/tex/meta.tex index d944b33..8296540 100644 --- a/tex/meta.tex +++ b/tex/meta.tex @@ -23,18 +23,18 @@ Emails: \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. +This paper addresses forecasting demand on a grid into the short-term future, + enabling, for example, predictive routing applications. 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. + both classical forecasting and machine learning methods + and adapt model evaluation and selection to typical demand: + intermittent with a double-seasonal pattern. +An empirical study shows that + an exponential smoothing based method trained on past demand data alone + achieves optimal accuracy, + if at least two months are on record. +With only a shorter demand history, + machine learning methods and real-time data may improve prediction. \end{abstract} \begin{keyword}