arXiv cs.AI· Chenghao Gu, Zhuokang Shen, Shenghao Wu, Tingxuan Zhu, Weiyi Luo, Haifeng Tang·· 14 小时前AI 评分27
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 通过按目标周各已知因子分别检索历史周并在固定预算下合并为因子参考，再用收缩误差与候选分歧控制两道门控，将因子参考与形状参考混合后修正骨干预测。在 Favorita、M5、Rossmann 和 Rohlik 上，FARE 使五个骨干模型的 WAPE 平均降低 11.46%、4.06%、14.63% 和 10.16%。
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