论文标题
分类预测方法的标准
Criteria for Classifying Forecasting Methods
论文作者
论文摘要
将预测方法分类为“机器学习”或“统计”性质的性质，在预测文献和社区的一部分中已变得司空见惯，这是M4竞争的例证以及组织者得出的结论。我们认为，这种区别并不源于分配给任一类的方法的根本差异。取而代之的是，这种区别可能具有部落性质，这限制了对不同预测方法的适当性和有效性的见解。我们提供了预测方法的替代特征，在我们看来，这些特征可以得出有意义的结论。此外，我们讨论了预测的领域，这些领域可能受益于ML与统计社区之间的交叉授粉。
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.
