arXiv Machine Learning By Yangyang Wang, Jiawei Gu, Li Long, Xin Li, Li Shen, Zhouyu Fu, Xiangjun Zhou, Xu Jiang

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail

Read the original on arXiv Machine Learning →

arXiv:2505. 16319v5 Announce Type: replace Abstract: Accurate demand estimation is critical for the retail business in guiding the inventory and pricing policies of perishable products.

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arXiv Machine Learning
Aug 27

CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition

CEDAR is a two‑stage framework for demand forecasting that incorporates planned actions and external event signals. Stage I uses an Action‑Interleaved Transformer to model controllable state transitions under interventions, while Stage II applies a Residual Correction Module that aligns event descriptions with product context using LLM‑assisted text representations. Experiments on a large Alibaba 1688 dataset show that CEDAR improves simulation accuracy over traditional time‑series forecasting baselines and benefits real‑world budget planning.

By Junjie Meng, Ranxu Zhang, Zi-an Zhang, Shujun Liu, Xiaoning Qi, Xiaozhou Xu, Yanyong Zhang, Hui Xiong, Chao Wang