arXiv cs.AI· Chenghao Gu, Zhuokang Shen, Shenghao Wu, Tingxuan Zhu, Weiyi Luo, Haifeng Tang·· 3 小时前AI 评分37
FARE：面向零售预测的因子感知检索与误差引导融合方法
FARE: Factor-Aware Retrieval with Error-Guided Fusion for Retail Forecasting
AI 导读
FARE（Factor-Aware Retrieval with Error-Guided Fusion）是一种零售销售预测方法，针对目标周各已知因子分别检索相似历史周，在固定预算下合并为因子参考，再由两级门控融合形状参考并校正骨干预测。
正文 · 原文
Abstract:Retail sales forecasting underpins inventory planning, yet promotions and calendar events reshape short-term demand. Although each factor usually has historical occurrences, the complete factor combination of a target week often has few precedents. The challenge is therefore to retrieve informative partial matches and weight them by their reliability, which varies across series. We propose FARE(Factor-Aware Retrieval with Error-Guided Fusion), which ranks historical weeks separately for each known target-week factor and merges the selected weeks into a factor reference under a fixed budget. Guided by shrunk errors from completed development weeks and the current disagreement among candidates, a first gate mixes this factor reference with a shape reference from similar sales histories, and a second gate controls how strongly the mixture corrects a backbone forecast. Both gates remain fixed during testing. On Favorita, M5, Rossmann, and Rohlik, FARE reduces the weighted absolute percentage error (WAPE) of five backbones by 11.46%, 4.06%, 14.63%, and 10.16% on average. With Chronos-2 frozen, FARE also outperforms conventional and existing enhancement methods, including supervised residual correction and retrieval augmentation. Code is available at this https URL .
| Comments: | 16 pages,3 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.40160 [cs.LG] |
| (or arXiv:2610.40160v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40160 arXiv-issued DOI via DataCite |
Submission history
From: Zhuokang Shen [view email]
[v1]
Thu, 29 Oct 2026 14:37:00 UTC (716 KB)
来源：arXiv cs.AI · arxiv.org