Rewrite dataset card in English

README.md

@@ -2,75 +2,110 @@
 license: other
 task_categories:
 - time-series-forecasting
+task_ids:
+- univariate-time-series-forecasting
+- multivariate-time-series-forecasting
+annotations_creators:
+- no-annotation
+source_datasets:
+- original
 tags:
 - time-series
+- forecasting
+- benchmark
+- fev
 - tsfile
+- apache-tsfile
+- modality:timeseries
+- Time-series
+- format:tsfile
+- arxiv:2509.26468
+size_categories:
+- 100K<n<1M
 pretty_name: walmart (TsFile format)
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "**/*.tsfile"
 ---
 
-# walmart — TsFile 格式
+# walmart (TsFile format)
 
-本目录是 [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets) 中 **walmart** 子集转换为 [Apache TsFile](https://tsfile.apache.org/) 格式的版本。
+This repository contains time-series forecasting data stored in [Apache TsFile](https://tsfile.apache.org/) format.
 
-## 来源与引用
+## Summary
 
-- **原始来源**：https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting
-- **论文/引用**：[[24]](https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting/overview/citation)
-- **统一格式合集**：[autogluon/fev_datasets](https://huggingface.co/datasets/autogluon/fev_datasets)
+- FEV subset: `walmart`
+- Unified source collection: [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets)
+- Original source: https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting
+- Paper / citation: [[24]](https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting/overview/citation)
+- Series: 2,936
+- Modalities: Time-series
+- TsFile rows (flattened observations): 4,609,143
+- Frequencies: source-defined
+- TsFile files: 1
+- Time precision: milliseconds (`INT64`).
 
-> 本数据由外部来源转换为统一格式后再转为 TsFile。许可与引用以**原始来源**为准，我们不对原始数据主张任何权利。除非另有说明，数据仅供研究用途。
+Licensing and citation requirements follow the original source. This repository does not claim ownership of the original data.
 
-## 数据统计
+## Dataset Statistics
 
-| 序列数 | 中位长度 | 观测点数 | 动态列 | 静态列 | 文件 |
-|---|---|---|---|---|---|
+| Series | Median series length | TsFile rows (observations) | Dynamic columns | Static columns | Data files |
+|---:|---:|---:|---:|---:|---|
 | 2,936 | 143 | 4,609,143 | 11 | 4 | `walmart.tsfile` |
 
-## TsFile 存储模型
+## Files
 
-- 每条原始序列（`id`）→ 一个 **device**（TAG 维度）。
-- 静态协变量列 → 也作 **TAG**（device 元数据）：`Store, Dept, Type, Size`。
-- 随时间变化的 target / 动态协变量 → **measurement**（FIELD）。
-- `timestamp` → `Time`（INT64 毫秒）。
-- 表名：walmart。
+The Hugging Face dataset card YAML points `configs.data_files` to all `*.tsfile` files in this repository.
 
-### 列含义
+- `walmart.tsfile`
 
-| 列 | 角色 | TsFile 类型 |
+## TsFile Storage Model
+
+- Each original series (`id`) is stored as one TsFile device.
+- Static covariate columns are stored as TAG columns: `Store, Dept, Type, Size`.
+- Time-varying targets and dynamic covariates are stored as FIELD measurements.
+- Source `timestamp` values are mapped to the TsFile `Time` column as millisecond timestamps.
+- Table name(s): walmart.
+
+### Column Schema
+
+| Column | Role | TsFile type |
 |---|---|---|
-| `Time` | Time（时间列） | INT64 |
-| `id` | TAG（device 维度） | STRING |
-| `Store` | TAG（device 维度） | DOUBLE |
-| `Dept` | TAG（device 维度） | DOUBLE |
-| `Type` | TAG（device 维度） | STRING |
-| `Size` | TAG（device 维度） | DOUBLE |
-| `target` | FIELD（measurement） | FLOAT |
-| `IsHoliday` | FIELD（measurement） | FLOAT |
-| `Temperature` | FIELD（measurement） | FLOAT |
-| `Fuel_Price` | FIELD（measurement） | FLOAT |
-| `MarkDown1` | FIELD（measurement） | FLOAT |
-| `MarkDown2` | FIELD（measurement） | FLOAT |
-| `MarkDown3` | FIELD（measurement） | FLOAT |
-| `MarkDown4` | FIELD（measurement） | FLOAT |
-| `MarkDown5` | FIELD（measurement） | FLOAT |
-| `CPI` | FIELD（measurement） | FLOAT |
-| `Unemployment` | FIELD（measurement） | FLOAT |
-
-> 注：有 2936 个原始 id 含非法标识符字符，已规范化为合法 device 名（如 10_1→_10_1, 10_10→_10_10, 10_11→_10_11）。
-
-## 转换说明
-
-- 每行原始数据是一整条序列 `(id, timestamp[], 各 target[])`，纵向打平为长表后写入 TsFile。
-- 数值类型按源列自适应：float32→FLOAT、float64→DOUBLE、整数→INT64、bool→BOOLEAN。
-- 时间精度：毫秒（INT64）。
-- 大表会被工具自动分片为 `<名>_1.tsfile`、`<名>_2.tsfile` …，同属一个逻辑表。
-
-## 读取示例
+| `Time` | Time column | INT64 |
+| `id` | TAG (device dimension) | STRING |
+| `Store` | TAG (device dimension) | DOUBLE |
+| `Dept` | TAG (device dimension) | DOUBLE |
+| `Type` | TAG (device dimension) | STRING |
+| `Size` | TAG (device dimension) | DOUBLE |
+| `target` | FIELD (measurement) | FLOAT |
+| `IsHoliday` | FIELD (measurement) | FLOAT |
+| `Temperature` | FIELD (measurement) | FLOAT |
+| `Fuel_Price` | FIELD (measurement) | FLOAT |
+| `MarkDown1` | FIELD (measurement) | FLOAT |
+| `MarkDown2` | FIELD (measurement) | FLOAT |
+| `MarkDown3` | FIELD (measurement) | FLOAT |
+| `MarkDown4` | FIELD (measurement) | FLOAT |
+| `MarkDown5` | FIELD (measurement) | FLOAT |
+| `CPI` | FIELD (measurement) | FLOAT |
+| `Unemployment` | FIELD (measurement) | FLOAT |
+
+> Note: 2936 original `id` values contained invalid identifier characters and were normalized to valid device names, for example 10_1→_10_1, 10_10→_10_10, 10_11→_10_11.
+
+## Conversion Notes
+
+- The source FEV format stores each time series as one nested row containing `id`, `timestamp[]`, and target or covariate arrays.
+- The TsFile conversion flattens those nested arrays into long rows. Therefore, the `TsFile rows` values above correspond to the number of timestamped observations after flattening.
+- TAG columns identify the device and static metadata. FIELD columns contain values that change over time.
+- Large logical tables may be split into multiple `.tsfile` shards such as `<name>_1.tsfile`, `<name>_2.tsfile`, and so on. Shards listed for the same frequency belong to the same logical table.
+
+## Reading Example
 
 ```python
 from tsfile import TsFileReader
 
 reader = TsFileReader("walmart.tsfile")
 schemas = reader.get_all_table_schemas()
-# 表名：walmart；列见下方"列含义"。
+# Table name(s): walmart
 ```