QUAL: Quantile Alignment Improves Zero-Shot Time-Series Foundation Models

Authors: Junhyeok Kang , Sangjun Han , Jun Seo , Seunghan Lee , Jaehoon Lee , Dongwan Kang , Minjae Kim , Sungdong Yoo , Tae Yoon Lim , Hwanil Choi , Soonyoung Lee , Wonbin Ahn

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Abstract: Time-series foundation models (TSFMs) provide zero-shot forecasts for unseen series, but their accuracy varies across datasets. Combining several models can reduce this variability, yet zero-shot deployment provides no target labels for selecting or weighting them. We find that a model pool is useful not only as a final ensemble, but also as a probability reference for transforming each member forecast. Aligning quantiles through this pool-relative reference improves every member forecast in aggregate. Motivated by this finding, we propose QUAL (Quantile alignment), a simple label-free framework for improving and combining diverse TSFMs. QUAL first produces aligned member forecasts, then applies Huber aggregation and rearrangement to obtain a robust combined forecast without training or model modification. On GIFT-Eval, quantile alignment reduces average member MASE and CRPS by 7.4% and 9.8%. The full framework remains robust when weak forecasters enter a candidate pool whose quality cannot be verified in advance. Code is available at this https URL .

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Accepted at the NeurIPS 2026 Workshop on Foundation Models for Temporal Systems: From Forecasting to World Modeling

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.41100 [cs.AI]

(or

arXiv:2610.41100v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.41100

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arXiv-issued DOI via DataCite (pending registration)

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From: Junhyeok Kang [ view email ]

[v1]

Fri, 30 Oct 2026 00:41:39 UTC (214 KB)

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