---
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: redset (TsFile format)
configs:
- config_name: default
  data_files:
  - split: train
    path: "**/*.tsfile"
---

# 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
```