Datasets:

smilegeng
/

fev_datasets

@@ -1,62 +1,62 @@
----
-license: other
-task_categories:
-- time-series-forecasting
-tags:
-- time-series
-- tsfile
-pretty_name: proenfo_bull (TsFile format)
----
-# proenfo_bull — TsFile 格式
-本目录是 [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets) 中 **proenfo_bull** 子集转换为 [Apache TsFile](https://tsfile.apache.org/) 格式的版本。
-## 来源与引用
-- **原始来源**：https://github.com/Leo-VK/EnFoAV
-- **论文/引用**：[[16]](https://doi.org/10.48550/arXiv.2307.07191)
-- **统一格式合集**：[autogluon/fev_datasets](https://huggingface.co/datasets/autogluon/fev_datasets)
-> 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。
-## 数据统计
-| 序列数 | 中位长度 | 观测点数 | 动态列 | 静态列 | 文件 |
-|---|---|---|---|---|---|
-| 41 | 17,544 | 2,877,216 | 4 | 0 | `proenfo_bull.tsfile` |
-## TsFile 存储模型
-- 每条原始序列（`id`）→ 一个 **device**（TAG 维度）。
-- 随时间变化的 target / 动态协变量 → **measurement**（FIELD）。
-- `timestamp` → `Time`（INT64 毫秒）。
-- 表名：proenfo_bull。
-### 列含义
-| 列 | 角色 | TsFile 类型 |
-|---|---|---|
-| `Time` | Time（时间列） | INT64 |
-| `id` | TAG（device 维度） | STRING |
-| `target` | FIELD（measurement） | FLOAT |
-| `airtemperature` | FIELD（measurement） | FLOAT |
-| `dewtemperature` | FIELD（measurement） | FLOAT |
-| `sealvlpressure` | FIELD（measurement） | FLOAT |
-## 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
-```python
-from tsfile import TsFileReader
-reader = TsFileReader("proenfo_bull.tsfile")
-schemas = reader.get_all_table_schemas()
-# 表名：proenfo_bull；列见下方"列含义"。
-```

+---
+license: other
+task_categories:
+- time-series-forecasting
+tags:
+- time-series
+- tsfile
+- format:tsfile
+pretty_name: proenfo_bull (TsFile format)
+---
+# proenfo_bull (TsFile format)
+## Source and Citation
+- **Original source**: https://github.com/Leo-VK/EnFoAV
+- **Paper / citation**: [[16]](https://doi.org/10.48550/arXiv.2307.07191)
+- **Unified source collection**: [autogluon/fev_datasets](https://huggingface.co/datasets/autogluon/fev_datasets)
+> This dataset was converted from external source data into a unified forecasting format and then into TsFile. Licensing and citation requirements follow the original source. We do not claim ownership of the original data. Unless otherwise stated, the data is intended for research use.
+## Dataset Statistics
+| Number of series | Median length | Observations | Dynamic columns | Static columns | Files |
+|---|---|---|---|---|---|
+| 41 | 17,544 | 2,877,216 | 4 | 0 | `proenfo_bull.tsfile` |
+## TsFile Storage Model
+- Each original series (`id`) is stored as one TsFile **device** (TAG dimension).
+- Time-varying targets and dynamic covariates are stored as **measurements** (FIELDs).
+- `timestamp` is mapped to `Time` as INT64 milliseconds.
+- Table name: proenfo_bull.
+### Column Schema
+| Column | Role | TsFile type |
+|---|---|---|
+| `Time` | Time column | INT64 |
+| `id` | TAG (device dimension) | STRING |
+| `target` | FIELD (measurement) | FLOAT |
+| `airtemperature` | FIELD (measurement) | FLOAT |
+| `dewtemperature` | FIELD (measurement) | FLOAT |
+| `sealvlpressure` | FIELD (measurement) | FLOAT |
+## Conversion Notes
+- Each original row stores one complete series `(id, timestamp[], target and dynamic arrays)`. The converter flattens these nested rows into a long table before writing TsFile.
+- Numeric types are inferred from the source columns, for example float32 to FLOAT, float64 to DOUBLE, integers to INT64, and bool to BOOLEAN.
+- Time precision: milliseconds (INT64).
+- Large tables may be automatically sharded as `<name>_1.tsfile`, `<name>_2.tsfile`, and so on. These shards belong to the same logical table.
+## Reading Example
+```python
+from tsfile import TsFileReader
+reader = TsFileReader("proenfo_bull.tsfile")
+schemas = reader.get_all_table_schemas()
+# Table name: proenfo_bull; see \"Column Schema\" below.
+```