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+#system ignore
+.DS_Store
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+# files by the editor
+*.swp
+*.vscode
+.ipynb_checkpoints
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README.md

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 # 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.
+## 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.
 
-## Fields' meaning
+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 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|
+|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.