Criteria for Classifying Forecasting Methods
This addresses a conceptual problem for forecasting researchers and practitioners by challenging a common classification that may hinder methodological progress.
The paper argues that classifying forecasting methods as 'machine learning' or 'statistical' is not based on fundamental differences and limits insights into their effectiveness, proposing alternative characteristics and discussing areas for cross-pollination between communities.
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.