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--- |
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language: |
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- en |
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license: cc-by-4.0 |
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task_categories: |
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- time-series-forecasting |
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tags: |
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- fresh-retail |
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- imputation |
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- forecasting |
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size_categories: |
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- 1M<n<10M |
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pretty_name: FreshRetailNet-50K |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train.parquet |
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- split: eval |
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path: data/eval.parquet |
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--- |
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# FreshRetailNet-50K |
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## Dataset Overview |
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FreshRetailNet-50K is the first industrial-grade time series dataset in the fresh retail domain, comprises 50,000 store-products which have hourly sales amount for 90 days, and **features about 20% organically out-of-stock data**. It also includes additional important information such as discounts, holiday status and various weather situations. This dataset is an ideal benchmark for future researches on time series imputation and forecasting techniques. |
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- [Technical Report](It will be posted later.) - Discover the methodology and technical details behind FreshRetailNet-50K. |
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- [Github Repo](It will be posted later.) - Access the complete pipeline used to train and evaluate. |
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This dataset is ready for commercial/non-commercial use. |
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## Data Fields |
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|Field|Type|Description| |
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|:---|:---|:---| |
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|city_id|int64|The encoded city id| |
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|store_id|int64|The encoded store id| |
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|management_group_id|int64|The encoded management group id| |
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|first_category_id|int64|The encoded first category id| |
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|second_category_id|int64|The encoded second category id| |
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|third_category_id|int64|The encoded third category id| |
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|product_id|int64|The encoded product id| |
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|dt|string|The date| |
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|sale_amount|float64|The daily sales amount after global normalization (Multiplied by a specific coefficient)| |
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|hours_sale|Sequence(float64)|The hourly sales amount after global normalization (Multiplied by a specific coefficient)| |
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|stock_hour6_22_cnt|int32|The number of out-of-stock hours between 6:00 and 22:00| |
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|hours_stock_status|Sequence(int32)|The hourly out-of-stock status| |
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|discount|float64|The discount rate (1.0 means no discount, 0.9 means 10% off)| |
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|holiday_flag|int32|Holiday indicator| |
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|activity_flag|int32|Activity indicator| |
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|precpt|float64|The total precipitation| |
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|avg_temperature|float64|The average temperature| |
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|avg_humidity|float64|The average humidity| |
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|avg_wind_level|float64|The average wind force| |
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### Hierarchical structure |
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- **warehouse**: city_id > store_id |
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- **product category**: management_group_id > first_category_id > second_category_id > third_category_id > product_id |
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## How to use it |
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You can load the dataset with the following lines of code. |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("Dingdong-Inc/FreshRetailNet-50K") |
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print(dataset) |
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DatasetDict({ |
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train: Dataset({ |
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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'], |
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num_rows: 4500000 |
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}) |
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eval: Dataset({ |
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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'], |
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num_rows: 350000 |
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}) |
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}) |
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``` |
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## License/Terms of Use |
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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. |
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**Data Developer:** Dingdong-Inc |
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### Use Case: <br> |
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Developers researching time series imputation and forecasting techniques. <br> |
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### Release Date: <br> |
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05/08/2025 <br> |
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## Data Version |
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1.0 (05/08/2025) |
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## Intended use |
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The FreshRetailNet-50K Dataset is intended to be freely used by the community to continue to improve time series imputation and forecasting techniques. |
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**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**. |
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## Citation |
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If you find the data useful, please cite: |
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``` |
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@article{2025freshretailnet-50k, |
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title={FreshRetailNet-50K: A Censored Demand Dataset with Stockout Interventions for Inventory-Aware Forecasting in Fresh Retail}, |
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author={Anonymous Author(s)}, |
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year={2025}, |
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eprint={2505.xxxxx}, |
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archivePrefix={arXiv}, |
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primaryClass={stat.ML}, |
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url={https://arxiv.org/abs/2505.xxxxx}, |
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} |
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``` |
