首页    期刊浏览 2025年12月19日 星期五
登录注册

文章基本信息

  • 标题:A Multi-Factor Analysis of Forecasting Methods: A Study on the M4 Competition
  • 本地全文:下载
  • 作者:Pantelis Agathangelou ; Demetris Trihinas ; Ioannis Katakis
  • 期刊名称:Data
  • 印刷版ISSN:2306-5729
  • 出版年度:2020
  • 卷号:5
  • 期号:2
  • 页码:41-64
  • DOI:10.3390/data5020041
  • 出版社:MDPI Publishing
  • 摘要:As forecasting becomes more and more appreciated in situations and activities of everyday life that involve prediction and risk assessment, more methods and solutions make their appearance in this exciting arena of uncertainty. However, less is known about what makes a promising or a poor forecast. In this article, we provide a multi-factor analysis on the forecasting methods that participated and stood out in the M4 competition, by focusing on Error (predictive performance), Correlation (among different methods), and Complexity (computational performance). The main goal of this study is to recognize the key elements of the contemporary forecasting methods, reveal what made them excel in the M4 competition, and eventually provide insights towards better understanding the forecasting task.
  • 关键词:forecasting; predictive analytics; statistics; machine learning forecasting ; predictive analytics ; statistics ; machine learning
国家哲学社会科学文献中心版权所有