Simrita/FreshRetailNet-50K

1---2language:3- en4license: cc-by-4.05task_categories:6- time-series-forecasting7tags:8- fresh-retail9- censored-demand10- hourly-stock-status11size_categories:12- 1M<n<10M13pretty_name: FreshRetailNet-50K14configs:15- config_name: default16  data_files:17  - split: train18    path: data/train.parquet19  - split: eval20    path: data/eval.parquet21---22 23# FreshRetailNet-50K24 25## Dataset Overview26FreshRetailNet-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.27 28- [Technical Report](https://arxiv.org/abs/2505.16319) - Discover the methodology and technical details behind FreshRetailNet-50K.29- [Github Repo](https://github.com/Dingdong-Inc/frn-50k-baseline) - Access the complete pipeline used to train and evaluate.30 31This dataset is ready for commercial/non-commercial use.32 33 34## Data Fields35|Field|Type|Description|36|:---|:---|:---|37|city_id|int64|The encoded city id|38|store_id|int64|The encoded store id|39|management_group_id|int64|The encoded management group id|40|first_category_id|int64|The encoded first category id|41|second_category_id|int64|The encoded second category id|42|third_category_id|int64|The encoded third category id|43|product_id|int64|The encoded product id|44|dt|string|The date|45|sale_amount|float64|The daily sales amount after global normalization (Multiplied by a specific coefficient)|46|hours_sale|Sequence(float64)|The hourly sales amount after global normalization (Multiplied by a specific coefficient)|47|stock_hour6_22_cnt|int32|The number of out-of-stock hours between 6:00 and 22:00|48|hours_stock_status|Sequence(int32)|The hourly out-of-stock status|49|discount|float64|The discount rate (1.0 means no discount, 0.9 means 10% off)|50|holiday_flag|int32|Holiday indicator|51|activity_flag|int32|Activity indicator|52|precpt|float64|The total precipitation|53|avg_temperature|float64|The average temperature|54|avg_humidity|float64|The average humidity|55|avg_wind_level|float64|The average wind force|56 57### Hierarchical structure58- **warehouse**: city_id > store_id59- **product category**: management_group_id > first_category_id > second_category_id > third_category_id > product_id60 61 62 63## How to use it64 65You can load the dataset with the following lines of code.66 67```python68from datasets import load_dataset69dataset = load_dataset("Dingdong-Inc/FreshRetailNet-50K")70print(dataset)71```72```text73DatasetDict({74    train: Dataset({75        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'],76        num_rows: 450000077    })78    eval: Dataset({79        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'],80        num_rows: 35000081    })82})83```84 85 86## License/Terms of Use87 88This 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.89 90**Data Developer:** Dingdong-Inc91 92 93### Use Case: <br>94Developers researching latent demand recovery and demand forecasting techniques. <br>95 96### Release Date:  <br>9705/08/2025 <br>98 99 100## Data Version1011.0 (05/08/2025)102 103 104## Intended use105 106The FreshRetailNet-50K Dataset is intended to be freely used by the community to continue to improve latent demand recovery and demand forecasting techniques.107**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**.108 109 110## Citation111 112If you find the data useful, please cite:113```114@article{2025freshretailnet-50k,115      title={FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail},116      author={Yangyang Wang, Jiawei Gu, Li Long, Xin Li, Li Shen, Zhouyu Fu, Xiangjun Zhou, Xu Jiang},117      year={2025},118      eprint={2505.16319},119      archivePrefix={arXiv},120      primaryClass={cs.LG},121      url={https://arxiv.org/abs/2505.16319},122}123```