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This arXiv paper proposes QUAL, a label-free method that aligns quantiles of time-series foundation model forecasts against a pool-relative reference, then combines them with robust aggregation. The reported gains on GIFT-Eval (about 7.4% lower MASE and 9.8% lower CRPS for members) are benchmark results from a single preprint, so they need independent replication. The main takeaway for ML practitioners is that ensembling zero-shot forecasters can improve accuracy without target labels, and that the method stays robust when some candidate models are weak.
QUAL: Quantile Alignment Improves Zero-Shot Time-Series Foundation Models
arXiv:2610.41100v1 Announce Type: new 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 https://github.com/junhyeokkang/QUAL.
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.
Artificial Intelligence (cs.AI)
arXiv:2610.41100 [cs.AI]
(or arXiv:2610.41100v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.41100 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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