Datasets:

Dingdong-Inc
/

FreshRetailNet-50K

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.

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:

Developers researching time series imputation and 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 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}, 
}