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README.md
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pretty_name: entsoe (TsFile format)
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# entsoe
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- **论文/引用**:[[6]](https://doi.org/10.25832/time_series/2020-10-06)
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- **统一格式合集**:[autogluon/fev_datasets](https://huggingface.co/datasets/autogluon/fev_datasets)
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| 频率 | 序列数 | 中位长度 | 观测点数 | 动态列 | 静态列 | 文件 |
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|---|---|---|---|---|---|---|
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| 15T | 6 | 175,292 | 6,310,512 | 6 | 0 | `15T/15T_1..15T_2.tsfile`
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| 1H | 6 | 43,822 | 1,577,592 | 6 | 0 | `1H/1H.tsfile` |
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| 30T | 6 | 87,645 | 3,155,220 | 6 | 0 | `30T/30T.tsfile` |
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## TsFile
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- `timestamp`
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###
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|---|---|---|
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| `Time` | Time
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| `id` | TAG
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| `target` | FIELD
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| `solar_generation_actual` | FIELD
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| `wind_onshore_generation_actual` | FIELD
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| `temperature` | FIELD
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| `radiation_direct_horizontal` | FIELD
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| `radiation_diffuse_horizontal` | FIELD
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##
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##
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```python
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from tsfile import TsFileReader
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reader = TsFileReader("15T/15T.tsfile")
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schemas = reader.get_all_table_schemas()
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#
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```
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pretty_name: entsoe (TsFile format)
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# entsoe (TsFile format)
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## Source and Citation
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- **Original source**: https://data.open-power-system-data.org/time_series/2020-10-06
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- **Paper / citation**: [[6]](https://doi.org/10.25832/time_series/2020-10-06)
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- **Unified source collection**: [autogluon/fev_datasets](https://huggingface.co/datasets/autogluon/fev_datasets)
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> 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.
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## Dataset Statistics
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| Frequency | Number of series | Median length | Observations | Dynamic columns | Static columns | Files |
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| 15T | 6 | 175,292 | 6,310,512 | 6 | 0 | `15T/15T_1..15T_2.tsfile` (2 shards) |
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| 1H | 6 | 43,822 | 1,577,592 | 6 | 0 | `1H/1H.tsfile` |
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| 30T | 6 | 87,645 | 3,155,220 | 6 | 0 | `30T/30T.tsfile` |
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## TsFile Storage Model
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- Each original series (`id`) is stored as one TsFile **device** (TAG dimension).
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- Time-varying targets and dynamic covariates are stored as **measurements** (FIELDs).
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- `timestamp` is mapped to `Time` as INT64 milliseconds.
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- Table names: entsoe_15T, entsoe_1H, entsoe_30T.
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### Column Schema
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| Column | Role | TsFile type |
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|---|---|---|
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| `Time` | Time column | INT64 |
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| `id` | TAG (device dimension) | STRING |
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| `target` | FIELD (measurement) | FLOAT |
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| `solar_generation_actual` | FIELD (measurement) | FLOAT |
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| `wind_onshore_generation_actual` | FIELD (measurement) | FLOAT |
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| `temperature` | FIELD (measurement) | FLOAT |
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| `radiation_direct_horizontal` | FIELD (measurement) | FLOAT |
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| `radiation_diffuse_horizontal` | FIELD (measurement) | FLOAT |
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## Conversion Notes
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- 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.
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- Numeric types are inferred from the source columns, for example float32 to FLOAT, float64 to DOUBLE, integers to INT64, and bool to BOOLEAN.
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- Time precision: milliseconds (INT64).
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- Large tables may be automatically sharded as `<name>_1.tsfile`, `<name>_2.tsfile`, and so on. These shards belong to the same logical table.
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## Reading Example
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```python
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from tsfile import TsFileReader
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reader = TsFileReader("15T/15T.tsfile")
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schemas = reader.get_all_table_schemas()
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# Table name: entsoe_15T; see \"Column Schema\" below.
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```
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