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README.md

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----
-license: apache-2.0
----
+---
+language:
+- en
+license: cc-by-4.0
+task_categories:
+- time-series-forecasting
+tags:
+- fresh-retail
+- imputation
+- forecasting
+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 description
+Our dataset represents the first industrial-grade time series dataset in the fresh retail domain, featuring 20% organically missing values.
+It includes hourly product sales and stock levels, along with additional information such as discounts, holiday status and weather situation.
+This dataset is an ideal benchmark for future researches on time series imputation and forecasting techniques.
+
+
+## Fields' meaning
+|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 data after global normalization (Multiplied by a specific coefficient)|
+|hours_sale|Sequence(float64)|the hourly sales data 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)
+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 time series imputation and 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 time series imputation and 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 Censored Demand Dataset with Stockout Interventions for Inventory-Aware Forecasting in Fresh Retail}, 
+      author={Anonymous Author(s)},
+      year={2025},
+      eprint={2505.xxxxx},
+      archivePrefix={arXiv},
+      primaryClass={stat.ML},
+      url={https://arxiv.org/abs/2505.xxxxx}, 
+}
+```

data/eval.parquet

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