arXiv AI November 2, 2026 By 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

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

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The paper introduces QUAL, a label‑free framework that improves zero‑shot time‑series foundation models by aligning their quantile forecasts using a pool of models as a probability reference. QUAL first aligns member forecasts, then aggregates them with Huber aggregation and rearrangement to produce a robust combined forecast without any training or model modification. Experiments on GIFT‑Eval show that this quantile alignment reduces average member MASE by 7.4% and CRPS by 9.8%, and the method remains robust even when weak forecasters are included in the candidate pool.

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