\subsection{Demand Forecasting with Machine Learning Methods}
\label{ml_methods}

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 (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
    intermittent demand.
Gradient Boosting, another popular ML method, was consistently outperformed by
    RFs, and artificial neural networks require an amount of data
    exceeding what our industry partner has by far.