Accurate demand estimation is critical for the retail business in guiding the inventory and pricing policies of perishable products. However, it faces fundamental challenges from censored sales data during stockouts, where unobserved demand creates systemic policy biases. Existing datasets lack the temporal resolution and annotations needed to address this censoring effect. To fill this gap, we present FreshRetailNet-50K, the first large-scale benchmark for censored demand estimation. It comprises 50,000 store-product time series of detailed hourly sales data from 898 stores in 18 major cities, encompassing 863 perishable SKUs meticulously annotated for stockout events. The hourly stock status records unique to this dataset, combined with rich contextual covariates, including promotional discounts, precipitation, and temporal features, enable innovative research beyond existing solutions. We demonstrate one such use case of two-stage demand modeling: first, we reconstruct the latent demand during stockouts using precise hourly annotations. We then leverage the recovered demand to train robust demand forecasting models in the second stage. Experimental results show that this approach achieves a 2.73% improvement in prediction accuracy while reducing the systematic demand underestimation from 7.37% to near-zero bias. With unprecedented temporal granularity and comprehensive real-world information, FreshRetailNet-50K opens new research directions in demand imputation, perishable inventory optimization, and causal retail analytics. The unique annotation quality and scale of the dataset address long-standing limitations in retail AI, providing immediate solutions and a platform for future methodological innovation. The data (https://huggingface.co/datasets/Dingdong-Inc/FreshRetailNet-50K) and code (https://github.com/Dingdong-Inc/frn-50k-baseline}) are openly released.

翻译：准确的需求估计对零售业务中易腐品的库存与定价策略制定至关重要。然而，该过程面临缺货期间销售数据被审查的根本性挑战——未被观测的需求会产生系统性政策偏差。现有数据集缺乏解决这种审查效应所需的时间粒度和标注信息。为填补这一空白，我们提出FreshRetailNet-50K，首个面向审查需求估计的大规模基准数据集。该数据集包含来自18个主要城市898家门店的50,000条门店-商品时间序列，涵盖863种易腐SKU的详细小时级销售数据，并对缺货事件进行了精细标注。该数据集独有的小时级库存状态记录，结合促销折扣、降水量及时序特征等丰富的上下文协变量，为超越现有方案的研究提供了创新空间。我们展示了一种两阶段需求建模的应用场景：首先利用精确的小时级标注重建缺货期间的隐性需求，再基于恢复的需求训练稳健的需求预测模型。实验结果表明，该方法将预测准确率提升了2.73%，同时将系统性的需求低估偏差从7.37%降至接近零水平。凭借前所未有的时间粒度和全面的真实世界信息，FreshRetailNet-50K为需求插补、易腐品库存优化及因果零售分析开辟了新研究方向。数据集的独特标注质量与规模解决了零售人工智能中长期存在的局限，为当前问题的解决及未来方法论创新提供了平台。数据(https://huggingface.co/datasets/Dingdong-Inc/FreshRetailNet-50K)与代码(https://github.com/Dingdong-Inc/frn-50k-baseline})已公开开源。