| --- |
| 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: |
| - forecasting |
| - benchmark |
| - fev |
| - arxiv:2509.26468 |
| - tsfile |
| - modality:timeseries |
| - timeseries |
| - format:tsfile |
| size_categories: |
| - n<1K |
| pretty_name: ercot (TsFile format) |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "**/*.tsfile" |
| modality: |
| - timeseries |
| --- |
| |
| # ercot (TsFile format) |
|
|
| This repository contains time-series forecasting data stored in [Apache TsFile](https://tsfile.apache.org/) format. |
|
|
| ## Summary |
|
|
| - FEV subset: `ercot` |
| - Unified source collection: [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets) |
| - Original source: https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy |
| - Series: 8 |
| - Modalities: Time-series |
| - TsFile rows (flattened observations): 1,299,648 |
| - Frequencies: 1D, 1H, 1M, 1W |
| - TsFile files: 5 |
| - Time precision: milliseconds (`INT64`). |
| |
| Licensing and citation requirements follow the original source. This repository does not claim ownership of the original data. |
| |
| ## Dataset Statistics |
| |
| | Frequency | Series | Median series length | TsFile rows (observations) | Dynamic columns | Static columns | Data files | |
| |---|---:|---:|---:|---:|---:|---| |
| | 1D | 8 | 6,452 | 51,616 | 1 | 0 | `1D/1D.tsfile` | |
| | 1H | 8 | 154,872 | 1,238,976 | 1 | 0 | `1H/1H_1..1H_2.tsfile` (2 shards) | |
| | 1M | 8 | 211 | 1,688 | 1 | 0 | `1M/1M.tsfile` | |
| | 1W | 8 | 921 | 7,368 | 1 | 0 | `1W/1W.tsfile` | |
| |
| ## Files |
| |
| The Hugging Face dataset card YAML points `configs.data_files` to all `*.tsfile` files in this repository. |
| |
| - `1D/1D.tsfile` |
| - `1H/1H_1.tsfile` |
| - `1H/1H_2.tsfile` |
| - `1M/1M.tsfile` |
| - `1W/1W.tsfile` |
| |
| ## TsFile Storage Model |
| |
| - Each original series (`id`) is stored as one TsFile device. |
| - 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): ercot_1D, ercot_1H, ercot_1M, ercot_1W. |
| |
| ### Column Schema |
| |
| | Column | Role | TsFile type | |
| |---|---|---| |
| | `Time` | Time column | INT64 | |
| | `id` | TAG (device dimension) | STRING | |
| | `target` | FIELD (measurement) | FLOAT | |
| |
| ## 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("1D/1D.tsfile") |
| schemas = reader.get_all_table_schemas() |
| # Table name(s): ercot_1D, ercot_1H, ercot_1M, ercot_1W |
| ``` |
| |
