Using dynamic loss weighting to boost improvements in forecast stability

Daan Caljon, Jeff Vercauteren, Simon De Vos, Wouter Verbeke and Jente Van Belle

International Journal of Forecasting, 2026, vol. 42, issue 2, 344-358

Abstract: Rolling origin forecast instability refers to variability in forecasts for a specific period induced by updating the forecast when new data points become available. Recently, an extension to the N-BEATS model for univariate time series point forecasting was proposed to include forecast stability as an additional optimization objective, next to accuracy. It was shown that more stable forecasts can be obtained without harming accuracy by minimizing a composite loss function that contains both a forecast error and a forecast instability component, with a static hyperparameter to control the impact of stability. In this paper, we empirically investigate whether further improvements in stability can be obtained without compromising accuracy by applying dynamic loss weighting algorithms, which change the loss weights during training. We show that existing dynamic loss weighting methods can achieve this objective, and provide insights into why this might be the case. Additionally, we propose an extension to the random weighting approach—task-aware random weighting—which also achieves this objective.

Keywords: Deep learning; Dynamic hyperparameter tuning; Rolling origin forecast instability; Global models; N-BEATS (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207025000615
Full text for ScienceDirect subscribers only

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:eee:intfor:v:42:y:2026:i:2:p:344-358

DOI: 10.1016/j.ijforecast.2025.07.002

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().