Datasets:

THULab
/

redset

@@ -1,101 +1,105 @@
----
-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
-size_categories:
-- n<1K
-pretty_name: redset (TsFile format)
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: "**/*.tsfile"
-modality:
-- timeseries
----
-# redset (TsFile format)
-This repository contains time-series forecasting data stored in [Apache TsFile](https://tsfile.apache.org/) format.
-## Summary
-- FEV subset: `redset`
-- Unified source collection: [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets)
-- Original source: https://github.com/amazon-science/redset/
-- Paper / citation: [[17]](https://www.amazon.science/publications/why-tpc-is-not-enough-an-analysis-of-the-amazon-redshift-fleet)
-- Series: 126 / 138 / 118
-- Modalities: Time-series
-- TsFile rows (flattened observations): 4,295,849
-- Frequencies: 15T, 1H, 5T
-- TsFile files: 6
-- 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 |
-|---|---:|---:|---:|---:|---:|---|
-| 15T | 126 | 8,640 | 1,052,371 | 1 | 1 | `15T/15T_1..15T_2.tsfile` (2 shards) |
-| 1H | 138 | 2,160 | 283,070 | 1 | 1 | `1H/1H.tsfile` |
-| 5T | 118 | 25,920 | 2,960,408 | 1 | 1 | `5T/5T_1..5T_3.tsfile` (3 shards) |
-## Files
-The Hugging Face dataset card YAML points `configs.data_files` to all `*.tsfile` files in this repository.
-- `15T/15T_1.tsfile`
-- `15T/15T_2.tsfile`
-- `1H/1H.tsfile`
-- `5T/5T_1.tsfile`
-- `5T/5T_2.tsfile`
-- `5T/5T_3.tsfile`
-## TsFile Storage Model
-- Each original series (`id`) is stored as one TsFile device.
-- Static covariate columns are stored as TAG columns: `subset`.
-- 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): redset_15T, redset_1H, redset_5T.
-### Column Schema
-| Column | Role | TsFile type |
-|---|---|---|
-| `Time` | Time column | INT64 |
-| `id` | TAG (device dimension) | STRING |
-| `subset` | 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("15T/15T_1.tsfile")
-schemas = reader.get_all_table_schemas()
-# Table name(s): redset_15T, redset_1H, redset_5T
-```

+---
+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:
+- 100K<n<1M
+pretty_name: redset (TsFile format)
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "**/*.tsfile"
+modality:
+- tabular
+- text
+- timeseries
+---
+# redset (TsFile format)
+This repository contains time-series forecasting data stored in [Apache TsFile](https://tsfile.apache.org/) format.
+## Summary
+- FEV subset: `redset`
+- Unified source collection: [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets)
+- Original source: https://github.com/amazon-science/redset/
+- Paper / citation: [[17]](https://www.amazon.science/publications/why-tpc-is-not-enough-an-analysis-of-the-amazon-redshift-fleet)
+- Series: 126 / 138 / 118
+- Modalities: Time-series
+- TsFile rows (flattened observations): 4,295,849
+- Frequencies: 15T, 1H, 5T
+- TsFile files: 6
+- 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 |
+|---|---:|---:|---:|---:|---:|---|
+| 15T | 126 | 8,640 | 1,052,371 | 1 | 1 | `15T/15T_1..15T_2.tsfile` (2 shards) |
+| 1H | 138 | 2,160 | 283,070 | 1 | 1 | `1H/1H.tsfile` |
+| 5T | 118 | 25,920 | 2,960,408 | 1 | 1 | `5T/5T_1..5T_3.tsfile` (3 shards) |
+## Files
+The Hugging Face dataset card YAML points `configs.data_files` to all `*.tsfile` files in this repository.
+- `15T/15T_1.tsfile`
+- `15T/15T_2.tsfile`
+- `1H/1H.tsfile`
+- `5T/5T_1.tsfile`
+- `5T/5T_2.tsfile`
+- `5T/5T_3.tsfile`
+## TsFile Storage Model
+- Each original series (`id`) is stored as one TsFile device.
+- Static covariate columns are stored as TAG columns: `subset`.
+- 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): redset_15T, redset_1H, redset_5T.
+### Column Schema
+| Column | Role | TsFile type |
+|---|---|---|
+| `Time` | Time column | INT64 |
+| `id` | TAG (device dimension) | STRING |
+| `subset` | 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("15T/15T_1.tsfile")
+schemas = reader.get_all_table_schemas()
+# Table name(s): redset_15T, redset_1H, redset_5T
+```