Criteria for Classifying Forecasting Methods

التفاصيل البيبلوغرافية
العنوان: Criteria for Classifying Forecasting Methods
المؤلفون: Januschowski, Tim, Gasthaus, Jan, Wang, Yuyang, Salinas, David, Flunkert, Valentin, Bohlke-Schneider, Michael, Callot, Laurent
سنة النشر: 2022
المجموعة: Computer Science
Statistics
مصطلحات موضوعية: Statistics - Machine Learning, Computer Science - Machine Learning
الوصف: 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.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2212.03523
رقم الأكسشن: edsarx.2212.03523
قاعدة البيانات: arXiv