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

Time-series foundation models (TSFMs) provide zero-shot forecasts for unseen series, but their accuracy varies across datasets.

By Kang, Han, Seo +9Oct 30, 2026

Score████░░░░░░3.6

Key numbers

7.4% and 9.8%

VerdictWorth a reader's time today.

Read the original PDF Code

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.

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

The editor's rubric

Heuristic review

Leverage4 / 5: A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).; 24% of this desk’s rubric
Magnitude3 / 5: Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.; 18% of this desk’s rubric
Evidence4 / 5: Strong: large scale, preregistered, independently replicated, or a well-powered randomized trial.; 14% of this desk’s rubric
Novelty3 / 5: A genuinely new approach to an open problem.; 16% of this desk’s rubric
Trajectory2 / 5: Some room to improve with obvious engineering.; 18% of this desk’s rubric
Stakes2 / 5: Benefits a professional community (practitioners, clinicians, engineers).; 10% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	████░4/5	24%	A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).
Magnitude	███░░3/5	18%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	████░4/5	14%	Strong: large scale, preregistered, independently replicated, or a well-powered randomized trial.
Novelty	███░░3/5	16%	A genuinely new approach to an open problem.
Trajectory	██░░░2/5	18%	Some room to improve with obvious engineering.
Stakes	██░░░2/5	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose, we report); breadth (zero/few-shot); gains (relative gain); verification (multiple benchmarks, held-out test, code released).

How the score was computed

rank-2026-10-18

Score████░░░░░░3.6

Score = 10 × (65% × adjusted merit / 10 + 25% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 6.2 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.9 / 10; Shrunk toward the desk prior by editor confidence (42%).
Attention: 0%; Citations, upvotes, points, mentions.
Freshness: 43%; Half-life decay since publication.

No attention signals recorded yet.

The record

Reviewed by heuristic-v10 on Nov 2, 2026, 02:05 UTC. Paper type: method.
Categories: cs.AI
TOP, No.7 in the Artificial Intelligence edition of November 2, 2026.