  Classifying forecasting methods as being either of a "machine learning" or
"statistical" nature has become commonplace in parts of the forecasting
literature and community, as exemplified by the M4 competition and the
conclusion drawn by the organizers. We argue that this distinction does not
stem from fundamental differences in the methods assigned to either class.
Instead, this distinction is probably of a tribal nature, which limits the
insights into the appropriateness and effectiveness of different forecasting
methods. We provide alternative characteristics of forecasting methods which,
in our view, allow to draw meaningful conclusions. Further, we discuss areas of
forecasting which could benefit most from cross-pollination between the ML and
the statistics communities.
