\subsection{Demand Forecasting with Classical Forecasting Methods}
\label{class_methods}

Forecasting became a formal discipline starting in the 1950s and has its
    origins in the broader field of statistics.
\cite{hyndman2018} provide a thorough overview of the concepts and methods
    established, and \cite{ord2017} indicate business-related applications
    such as demand forecasting.
These "classical" forecasting methods share the characteristic that they are
    trained over the entire $Y$ first.
Then, for prediction, the forecaster specifies the number of time steps for
    which he wants to generate forecasts.
That is different for ML models.