Duplicate from Dingdong-Inc/FreshRetailNet-50K

Co-authored-by: Jarvis Gu <JarvisGu@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,61 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+data/train.parquet filter=lfs diff=lfs merge=lfs -text
+data/eval.parquet filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,10 @@
+#system ignore
+.DS_Store
+Thumbs.db
+.idea
+
+# files by the editor
+*.swp
+*.vscode
+.ipynb_checkpoints
+

README.md

@@ -0,0 +1,123 @@
+---
+language:
+- en
+license: cc-by-4.0
+task_categories:
+- time-series-forecasting
+tags:
+- fresh-retail
+- censored-demand
+- hourly-stock-status
+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 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](https://arxiv.org/abs/2505.16319) - Discover the methodology and technical details behind FreshRetailNet-50K.
+- [Github Repo](https://github.com/Dingdong-Inc/frn-50k-baseline) - 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)
+```
+```text
+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 latent demand recovery and demand 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 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},
+}
+```

data/eval.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b118840664280c6b88bffc84c80ee1f54c05d911e354b7599e5da10995e960e
+size 8440124

data/train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6706832db892bbae4969c19d87e07975d2543d2ba7d7d4756360654785de5a3d
+size 106436287