A novel ranking procedure for forecasting approaches using Data Envelopment Analysis

Ali Emrouznejad*, Bahman Rostami-Tabar, Konstantinos Petridis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


To compare the accuracy of different forecasting approaches an error measure is required. Many error measures have been proposed in the literature, however in practice there are some situations where different measures yield different decisions on forecasting approach selection and there is no agreement on which approach should be used. Generally forecasting measures represent ratios or percentages providing an overall image of how well fitted the forecasting technique is to the observations. This paper proposes a multiplicative Data Envelopment Analysis (DEA) model in order to rank several forecasting techniques. We demonstrate the proposed model by applying it to the set of yearly time series of the M3 competition. The usefulness of the proposed approach has been tested using the M3-competition where five error measures have been applied in and aggregated to a single DEA score.

Original languageEnglish
JournalTechnological Forecasting and Social Change
Early online date5 Aug 2016
Publication statusPublished - Oct 2016

Bibliographical note

© 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).


  • accuracy measure
  • data envelopment analysis
  • forecasting


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