Simrita/FreshRetailNet-50K
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--- 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 863 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 许可协议：CC BY 4.0 任务类别： - 时间序列预测（time-series forecasting） 标签： - 生鲜零售（fresh-retail） - 删失需求（censored-demand） - 小时级库存状态（hourly-stock-status） 样本规模区间： - 100万 < 样本量 < 1000万 数据集名称：FreshRetailNet-50K 配置项： - 配置名称：默认 数据文件： - 拆分方式：训练集 路径：data/train.parquet - 拆分方式：验证集 路径：data/eval.parquet # FreshRetailNet-50K ## 数据集概览 FreshRetailNet-50K是生鲜零售领域首个面向删失需求（censored demand）估计的大规模基准数据集，**包含约20%的自然发生的缺货数据**。该数据集涵盖来自18个主要城市898家门店的50000组门店-商品90天时长的细粒度小时级销售时序数据，包含863个易逝品库存保有单位（SKU, perishable SKUs），并附带精细的缺货事件标注。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各类时序特征，可为突破现有解决方案的研究提供支撑。 - [技术报告](https://arxiv.org/abs/2505.16319) —— 了解FreshRetailNet-50K背后的方法学与技术细节。 - [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|数据类型|说明| |:---|:---|:---| |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|经过全局归一化的日销售额（已乘以特定系数）| |hours_sale|Sequence(float64)|经过全局归一化的小时级销售额序列（已乘以特定系数）| |stock_hour6_22_cnt|int32|6:00至22:00期间的缺货小时数| |hours_stock_status|Sequence(int32)|小时级缺货状态序列| |discount|float64|折扣率（1.0代表无折扣，0.9代表九折）| |holiday_flag|int32|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|平均湿度| |avg_wind_level|float64|平均风力等级| ### 层级结构 - **仓储层级**：城市ID > 门店ID - **商品品类层级**：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({ 训练集: Dataset({ 特征字段: ['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'], 样本数量: 4500000 }) 验证集: Dataset({ 特征字段: ['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'], 样本数量: 350000 }) }) ## 许可与使用条款 本数据集采用知识共享署名4.0国际许可协议（CC BY 4.0），详情可访问 https://creativecommons.org/licenses/by/4.0/legalcode。 **数据开发者**：Dingdong-Inc ### 适用场景 面向开展潜在需求恢复与需求预测技术研究的开发者。 ### 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数据集面向所有社区开发者免费开放，用于持续改进潜在需求恢复与需求预测技术。**请注意，使用者需自行核查本数据集许可是否适配其预期使用场景**。 ## 引用声明 若您觉得本数据集对您的研究有所帮助，请引用以下文献： bibtex @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}, }
