arXiv · cs.AI · 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 · 10 小时前模型评分45
分位数对齐（QUAL，Quantile Alignment）：分位数对齐提升零样本时间序列基础模型
QUAL: Quantile Alignment Improves Zero-Shot Time-Series Foundation Models
导读
研究者提出免标签框架分位数对齐（QUAL，Quantile Alignment），利用模型池作为概率参考对齐各成员预测，再经Huber聚合与重排得到稳健组合预测，无需训练或改动模型。
分类置信度
中 · 待人工复核
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来源级别：媒体与个人
事件类型：研究发布
证据摘录
- “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.”
- “Code is available at this https URL .”
来源：arXiv · cs.AI · arxiv.org