Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting

Pedro Henrique Melo Albuquerque, Yaohao Peng and João Pedro Fontoura da Silva

Journal of Forecasting, 2022, vol. 41, issue 8, 1701-1724

Abstract: This paper discusses the application of ensemble techniques for the prediction of time series, presenting an in‐depth review of the main techniques and algorithms used by the recent literature, with emphasis on the bootstrap aggregation (bagging) and boosting approaches. We also discuss the theoretical foundations of the ensemble‐based models, presenting measures of model stability and the main aggregation methods to combine the forecasts of the individual models, as well as recommendations for future developments for related research agendas.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://doi.org/10.1002/for.2894

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:wly:jforec:v:41:y:2022:i:8:p:1701-1724

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().