Ensembling Shrunk Weight Estimations in Forecast Combination

Veronika Wachslander () and Thomas Setzer
Additional contact information
Veronika Wachslander: Catholic University of Eichstätt-Ingolstadt
Thomas Setzer: Catholic University of Eichstätt-Ingolstadt

A chapter in Operations Research Proceedings 2024, 2025, pp 39-45 from Springer

Abstract: Abstract In most fields of business, accurate predictions are the basis for future planning, whereby combining predictions of different forecasters and/or forecasting models usually generates more accurate predictions than any individual forecaster or model alone. While simply averaging forecasts using equal weights (EW) has proven to be a robust strategy in practice, an alternative approach applied in several recent papers is to learn so-called optimal weights (OW), that minimize the mean squared error (MSE) on past (training) data, and shrinking these weights towards EW. This strategy aims to learn structures from training data while mitigating overfitting and to avoid high prediction errors with novel forecasts. However, estimating OW and shrinkage levels on training samples is still subject to uncertainty and can be highly unstable especially for smaller datasets and larger sets of forecasters. This turns out to be a key problem of such approaches, which usually do not systematically beat EW approaches in practical settings. We introduce a new procedure to obtain more stable weighting schemes. The procedure learns OW on randomly drawn subsets of the training data and determines the optimal shrinkage towards EW on the respective omitted observations, resulting in varying shrunk weight vectors. Subsequently, these vectors are averaged so that the final weight each forecaster receives corresponds to the average (shrunk) optimal weight over all subsets and is asymptotically less extreme. We evaluate the procedure on synthetic datasets, where it shows benefits compared to EW as well as OW approaches in terms of the out-of-sample MSE.

Keywords: Forecast combination; Weight shrinkage; Subset averaging (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:lnopch:978-3-031-92575-7_6

Ordering information: This item can be ordered from
http://www.springer.com/9783031925757

DOI: 10.1007/978-3-031-92575-7_6

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().