A multi-factor analysis of forecasting methods: A study on the M4 competition

Pantelis Agathangelou, Demetris Trihinas, Ioannis Katakis

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number41
JournalData
Volume5
Issue number2
DOIs
Publication statusPublished - Apr 2020

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Keywords

  • Forecasting
  • Machine learning
  • Predictive analytics
  • Statistics

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