Datasets:
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---
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 Overview
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.
- [Technical Report](It will be posted later.) - Discover the methodology and technical details behind FreshRetailNet-50K.
- [Github Repo](It will be posted later.) - 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)
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},
}
``` |