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arXiv · 2610.40160

FARE: Factor-Aware Retrieval with Error-Guided Fusion for Retail Forecasting

Chenghao Gu·Zhuokang Shen·Shenghao Wu·Tingxuan Zhu·Weiyi Luo·Haifeng Tang
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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 https://github.com/HousonGu/FARE .

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Chenghao Gu, Zhuokang Shen, Shenghao Wu, Tingxuan Zhu, Weiyi Luo, Haifeng Tang. 2026-10-29. FARE: Factor-Aware Retrieval with Error-Guided Fusion for Retail Forecasting. https://arxiv.org/abs/2610.40160

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