wangdx25/freshretailnet_50k

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 865 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 - Discover the methodology and technical details behind FreshRetailNet-50K.
• Github Repo - 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

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:

Developers researching latent demand recovery and demand forecasting techniques.

Release Date:

05/08/2025

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},
}

TsFile Conversion

• Original dataset: Dingdong-Inc/FreshRetailNet-50K

• Modalities: Time-series

• Converted data files are listed in the YAML metadata above.

• Source README text and dataset-specific metadata are retained; the source Usage section is replaced with the executable TsFile Python SDK example below.

• The source dt remains represented losslessly as Time at midnight UTC.

• hours_sale and hours_stock_status are fixed-width 24-hour sequences flattened to *_00 through *_23; all daily scalar fields are retained.

• train: 659,405,814 bytes, 180 TsFile shard(s)

• eval: 354,737,596 bytes, 14 TsFile shard(s)

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("freshretailnet_50k_eval_1.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())