arXiv:2610.40160v1 Announce Type: cross
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 .
arxiv
FARE: Factor-Aware Retrieval with Error-Guided Fusion for Retail Sales Forecasting
FARE is a retrieval-augmentation method for short-term retail demand forecasting, where target weeks often combine promotion and calendar factors that have few exact historical precedents. It ranks historical weeks separately for each known target-week factor, merges the top matches into a factor reference under a fixed budget, and blends that with a shape reference drawn from similar sales histories. Two gating mechanisms, driven by shrunk errors from completed development weeks and by current disagreement among candidates, set the mix and control how strongly it corrects a backbone forecast; both gates stay fixed at test time. Across Favorita, M5, Rossmann and Rohlik, FARE cut WAPE of five backbones by 11.46%, 4.06%, 14.63% and 10.16% on average, and also beat supervised residual correction and retrieval augmentation with Chronos-2 frozen. Code is released publicly.
It offers a concrete, code-backed retrieval-and-gating recipe that measurably improves multiple forecasting backbones on four standard retail benchmarks, which is directly actionable for demand-planning practitioners.
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