Does a meta-combining method lead to more accurate forecasts in the decision-making process?

Emrah Gulay () and Serkan Aras ()

Operations Research and Decisions, 2024, vol. 34, issue 3, 101-124

Abstract: To improve forecasting accuracy, researchers employed various combination techniques for a long time. When researchers deal with time series data by using dissimilar models, the combined forecasts of these models are expected to be superior. Deriving a weighting scheme performing better than simple but hard−to−beat combining methods has always been challenging. In this study, a new weighting method based on the hybridisation of combining algorithms is proposed. Five popular datasets were utilised to demonstrate the effectiveness of the proposed method in an out-of-sample context. The results indicate that the proposed method leads to more accurate forecasts than other combining techniques used in the study.

Keywords: forecasting; combined forecasts; time series; meta−combining (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ord.pwr.edu.pl/assets/papers_archive/ord2024vol34no3_6.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wut:journl:v:34:y:2024:i:3:p:101-124:id:6

DOI: 10.37190/ord240306

Access Statistics for this article

More articles in Operations Research and Decisions from Wroclaw University of Science and Technology, Faculty of Management Contact information at EDIRC.
Bibliographic data for series maintained by Adam Kasperski ().