Retail Demand Forecasting

Latest papers

14 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

FARE: Factor-Aware Retrieval with Error-Guided Fusion for Retail Forecasting
Oct 29, 2026Chenghao Gu, Zhuokang Shen, Shenghao Wu +3Retail Demand Forecasting Time Series Forecasting
East China Normal University, Shanghai, China · China Merchants Bank Credit Card Center, Shanghai, China
World Models, Trees and Foundation Models for Retail Forecasting: Evidence from Four Public Data Sets
Oct 18, 2026Karthik Iyer, Madan DabbeeruRetail Demand Forecasting Time Series Forecasting
CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition
Aug 26, 2026Junjie Meng, Ranxu Zhang, Zi-an Zhang +6Time Series Forecasting Retail Demand Forecasting
School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, China · Alibaba Group, Hangzhou, China · Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China +1
ReasonCast: Agentic Demand Forecasting with Selective Semantic Reasoning
Aug 15, 2026Ziyue Yang, Chaolin Xu, Yijing Wang +5Retail Demand Forecasting Time Series Forecasting
Taobao and Tmall Group, Alibaba Group China
Demand Transfer Estimation at Scale via Restricted Logit Modeling
Aug 13, 2026Lakshya Garg, Deep Narayan Mishra, Swapnil Yadav +4Discrete Choice Modeling Retail Demand Forecasting
Walmart Global Tech, Sunnyvale, CA, USA
Contextual Deconvolution for Variance-Stable Demand Sensing: Kernel-Modulated Operators in Promotional Retail
Jul 28, 2026Mohammad ForouheshInventory Control Time Series Forecasting
Amirkabir University of Technology, Tehran, Iran
A Predict-then-Correct Loop Based on Few-Shot Continuous Contextual Bandit for Demand Forecasting
Jul 17, 2026Zhiwei Lei, Benedict Jun Ma, Ilya JacksonTime Series Forecasting Contextual Bandits
Thrust of Intelligent Transportation, Hong Kong University of Science and Technology (Guangzhou), China · Center for Transportation and Logistics, Massachusetts Institute of Technology, USA
Accuracy-Preserving Stability Regularization for Large-Scale Retail Demand Forecasting
Jul 14, 2026Jize Li, Jiani He, Dishu Yang +3Time Series Forecasting Retail Demand Forecasting
Boston University, Boston, MA, USA · Massachusetts Institute of Technology, Cambridge, MA, USA · Northeastern University, San Jose, CA, USA +3
Optimizing ARDL Models for Retail Sales Forecasting and Fair Pricing
Jul 10, 2026Sujay Uday RittikarTime Series Forecasting Algorithmic Fairness
Department of Applied Computer Science The University of Winnipeg Winnipeg, MB, Canada
GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets
Jun 26, 2026Janak M. Patel, Anirudh Deodhar, Dagnachew BirruMultivariate Time Series Forecasting Long-Term Time Series Forecasting
Phi Labs, Quantiphi Marlborough, MA 01752, USA
LLM-Powered Virtual Population for Demand Simulation and Pricing
Jun 15, 2026Chengpiao Huang, Kaizheng WangUser Modeling Dynamic Pricing
Department of Industrial Engineering and Operations Research, Columbia University. · Department of Industrial Engineering and Operations Research and Data Science Institute, Columbia University.
FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting
Jun 8, 2026Qianyang Li, Xingjun Zhang, Shaoxun Wang +2Sparse Mixture-of-Experts Time Series Forecasting
School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China · Department of Research and Development, Shandong New Beiyang Information Technology Co., Ltd., WeiHai 264200, China · Department of Computer Science and Technology, Tsinghua University, Beijing 100084
Integrable Elasticity via Neural Demand Potentials
May 21, 2026Carlos Heredia, Daniel RoncelRetail Demand Forecasting
IAMM Research, Department of Applied Artificial Intelligence DAMM, Carrer del Rossell´o 515, 08025 Barcelona, Catalonia, Spain
Switch-Hurdle: A MoE Encoder with AR Hurdle Decoder for Intermittent Demand Forecasting
Feb 26, 2026Fabian Muşat, Simona CăbuzTime Series Forecasting Retail Demand Forecasting
eMAG, Romania

Oct 29, 2026J/K move · Enter open · S save

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

Chenghao Gu, Zhuokang Shen, Shenghao Wu, Tingxuan Zhu+2

East China Normal University, Shanghai, China · China Merchants Bank Credit Card Center, Shanghai, China

Retail Demand Forecasting Time Series Forecasting Retrieval-Augmented Time Series Forecasting Time Series Forecasting with Covariates

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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 .