Melody1960/FreshRetailNet-50K

--- language: - en license: cc-by-4.0 task_categories: - time-series-forecasting tags: - fresh-retail - censored-demand - hourly-stock-status size_categories: - 1M<n<10M pretty_name: FreshRetailNet-50K configs: - config_name: default data_files: - split: train path: data/train.parquet - split: eval path: data/eval.parquet --- # FreshRetailNet-50K ## Dataset Overview FreshRetailNet-50K is the first large-scale benchmark for censored demand estimation in the fresh retail domain, **incorporating approximately 20% organically occurring stockout data**. It comprises 50,000 store-product 90-day time series of detailed hourly sales data from 898 stores in 18 major cities, encompassing 865 perishable SKUs with meticulous stockout event annotations. The hourly stock status records unique to this dataset, combined with rich contextual covariates including promotional discounts, precipitation, and other temporal features, enable innovative research beyond existing solutions. - [Technical Report](https://arxiv.org/abs/2505.16319) - Discover the methodology and technical details behind FreshRetailNet-50K. - [Github Repo](https://github.com/Dingdong-Inc/frn-50k-baseline) - Access the complete pipeline used to train and evaluate. This dataset is ready for commercial/non-commercial use. ## Data Fields |Field|Type|Description| |:---|:---|:---| |city_id|int64|The encoded city id| |store_id|int64|The encoded store id| |management_group_id|int64|The encoded management group id| |first_category_id|int64|The encoded first category id| |second_category_id|int64|The encoded second category id| |third_category_id|int64|The encoded third category id| |product_id|int64|The encoded product id| |dt|string|The date| |sale_amount|float64|The daily sales amount after global normalization (Multiplied by a specific coefficient)| |hours_sale|Sequence(float64)|The hourly sales amount after global normalization (Multiplied by a specific coefficient)| |stock_hour6_22_cnt|int32|The number of out-of-stock hours between 6:00 and 22:00| |hours_stock_status|Sequence(int32)|The hourly out-of-stock status| |discount|float64|The discount rate (1.0 means no discount, 0.9 means 10% off)| |holiday_flag|int32|Holiday indicator| |activity_flag|int32|Activity indicator| |precpt|float64|The total precipitation| |avg_temperature|float64|The average temperature| |avg_humidity|float64|The average humidity| |avg_wind_level|float64|The average wind force| ### Hierarchical structure - **warehouse**: city_id > store_id - **product category**: management_group_id > first_category_id > second_category_id > third_category_id > product_id ## How to use it You can load the dataset with the following lines of code. ```python from datasets import load_dataset dataset = load_dataset("Dingdong-Inc/FreshRetailNet-50K") print(dataset) ``` ```text DatasetDict({ train: Dataset({ features: ['city_id', 'store_id', 'management_group_id', 'first_category_id', 'second_category_id', 'third_category_id', 'product_id', 'dt', 'sale_amount', 'hours_sale', 'stock_hour6_22_cnt', 'hours_stock_status', 'discount', 'holiday_flag', 'activity_flag', 'precpt', 'avg_temperature', 'avg_humidity', 'avg_wind_level'], num_rows: 4500000 }) eval: Dataset({ features: ['city_id', 'store_id', 'management_group_id', 'first_category_id', 'second_category_id', 'third_category_id', 'product_id', 'dt', 'sale_amount', 'hours_sale', 'stock_hour6_22_cnt', 'hours_stock_status', 'discount', 'holiday_flag', 'activity_flag', 'precpt', 'avg_temperature', 'avg_humidity', 'avg_wind_level'], num_rows: 350000 }) }) ``` ## License/Terms of Use This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0) available at https://creativecommons.org/licenses/by/4.0/legalcode. **Data Developer:** Dingdong-Inc ### Use Case: <br> Developers researching latent demand recovery and demand forecasting techniques. <br> ### Release Date: <br> 05/08/2025 <br> ## Data Version 1.0 (05/08/2025) ## Intended use The FreshRetailNet-50K Dataset is intended to be freely used by the community to continue to improve latent demand recovery and demand forecasting techniques. **However, for each dataset an user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose**. ## Citation If you find the data useful, please cite: ``` @article{2025freshretailnet-50k, title={FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail}, author={Yangyang Wang, Jiawei Gu, Li Long, Xin Li, Li Shen, Zhouyu Fu, Xiangjun Zhou, Xu Jiang}, year={2025}, eprint={2505.16319}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2505.16319}, } ```--- 语言： - 英语 许可证：CC BY 4.0 任务类别： - 时间序列预测 标签： - 生鲜零售 - 截尾需求（censored demand） - 小时级库存状态 数据规模： - 100万 < 数据量 < 1000万 友好名称：FreshRetailNet-50K 配置项： - 配置名称：默认 数据文件： - 划分：训练集 路径：data/train.parquet - 划分：验证集 路径：data/eval.parquet --- # FreshRetailNet-50K ## 数据集概述 FreshRetailNet-50K是生鲜零售领域首个面向截尾需求（censored demand）估计的大规模基准数据集，包含约20%的自然发生的售罄数据。该数据集涵盖来自18个核心城市898家门店的5万条门店-商品级90天时长的细粒度小时级销售时序数据，包含865个易变质SKU，并附带精细标注的售罄事件记录。本数据集独有的小时级库存状态记录，结合促销折扣、降水等丰富的上下文协变量与时间特征，为突破现有解决方案的创新研究提供了支撑。 - [技术报告](https://arxiv.org/abs/2505.16319) —— 了解FreshRetailNet-50K背后的方法论与技术细节。 - [GitHub仓库](https://github.com/Dingdong-Inc/frn-50k-baseline) —— 获取用于训练与评估的完整流程代码。 本数据集可用于商业与非商业用途。 ## 数据字段 |字段名|数据类型|字段描述| |:---|:---|:---| |city_id|int64|编码后的城市ID| |store_id|int64|编码后的门店ID| |management_group_id|int64|编码后的管理组ID| |first_category_id|int64|编码后的一级品类ID| |second_category_id|int64|编码后的二级品类ID| |third_category_id|int64|编码后的三级品类ID| |product_id|int64|编码后的商品ID| |dt|string|日期| |sale_amount|float64|经全局归一化后的日销售额（已乘以特定系数）| |hours_sale|Sequence(float64)|经全局归一化后的小时销售额序列（已乘以特定系数）| |stock_hour6_22_cnt|int32|6:00至22:00期间的售罄小时数| |hours_stock_status|Sequence(int32)|小时级库存状态序列| |discount|float64|折扣率（1.0代表无折扣，0.9代表9折优惠）| |holiday_flag|int32|节假日标识| |activity_flag|int32|营销活动标识| |precpt|float64|总降水量| |avg_temperature|float64|平均气温| |avg_humidity|float64|平均相对湿度| |avg_wind_level|float64|平均风力等级| ### 层级结构 - **仓储拓扑层级**：城市ID > 门店ID - **商品分类层级**：管理组ID > 一级品类ID > 二级品类ID > 三级品类ID > 商品ID ## 使用方法 你可以通过以下代码加载该数据集。 python from datasets import load_dataset dataset = load_dataset("Dingdong-Inc/FreshRetailNet-50K") print(dataset) text DatasetDict({ train: Dataset({ features: ['city_id', 'store_id', 'management_group_id', 'first_category_id', 'second_category_id', 'third_category_id', 'product_id', 'dt', 'sale_amount', 'hours_sale', 'stock_hour6_22_cnt', 'hours_stock_status', 'discount', 'holiday_flag', 'activity_flag', 'precpt', 'avg_temperature', 'avg_humidity', 'avg_wind_level'], num_rows: 4500000 }) eval: Dataset({ features: ['city_id', 'store_id', 'management_group_id', 'first_category_id', 'second_category_id', 'third_category_id', 'product_id', 'dt', 'sale_amount', 'hours_sale', 'stock_hour6_22_cnt', 'hours_stock_status', 'discount', 'holiday_flag', 'activity_flag', 'precpt', 'avg_temperature', 'avg_humidity', 'avg_wind_level'], num_rows: 350000 }) }) ## 使用许可证条款 本数据集采用知识共享署名4.0国际许可协议（CC BY 4.0），完整条款请见：https://creativecommons.org/licenses/by/4.0/legalcode。 **数据开发者**：Dingdong-Inc ### 适用场景： 面向潜在需求恢复与需求预测技术的研发人员。 ### 发布日期： 2025年5月8日 ## 数据版本 1.0（2025年5月8日） ## 预期用途 FreshRetailNet-50K数据集面向全球社区免费开放，用于持续优化潜在需求恢复与需求预测技术。**请注意，使用者需自行确认所用数据集的许可证是否符合其预期使用场景。** ## 引用 若您认为本数据集对研究有所帮助，请引用以下文献： @article{2025freshretailnet-50k, title={FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail}, author={Yangyang Wang, Jiawei Gu, Li Long, Xin Li, Li Shen, Zhouyu Fu, Xiangjun Zhou, Xu Jiang}, year={2025}, eprint={2505.16319}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2505.16319}, }