---
language:
- en
license: cc-by-4.0
size_categories:
- 10M<n<100M
task_categories:
- time-series-forecasting
tags:
- forecasting
- application-traffic
- cloud-computing
- benchmark
- TSF-regime
- regime-balanced
- single-provenance
- iotdb
- tsfile
- format:tsfile
- timeseries
pretty_name: QuitoBench (TsFile)
---





# QuitoBench — TsFile

> **Converted to [Apache TsFile](https://tsfile.apache.org/) format** from the
> original Hugging Face dataset
> [`hq-bench/quitobench`](https://huggingface.co/datasets/hq-bench/quitobench).
> The dataset description below follows the original card. License:
> **CC-BY-4.0** (same as source). Values are unchanged; this is a format
> conversion only.

**QuitoBench** is a regime-balanced evaluation benchmark curated from **Quito**, a
billion-scale, single-provenance time series dataset of application-traffic
workloads from Alipay's production platform.

> 🌐 Project Page: [hq-bench.github.io/quito](https://hq-bench.github.io/quito/)
> 📄 Paper: [arXiv:2603.26017](https://arxiv.org/abs/2603.26017)
> 💻 Code: [github.com/alipay/quito](https://github.com/alipay/quito)
> 📦 Training Corpus: [hq-bench/quito-corpus](https://huggingface.co/datasets/hq-bench/quito-corpus)

## Dataset Overview

| | `hour` config | `min` config |
|---|---|---|
| Granularity | 1 hour | 10 minutes |
| # test series | 517 | 773 |
| Series length | 15,356 steps | 5,904 steps |
| Test-set length / series | 552 steps | 3,312 steps |
| Date range | 2021-11-18 → 2023-08-19 | 2023-07-10 → 2023-08-19 |
| # variates / series | 5 | 5 |
| Total rows | 7,939,052 | 4,563,792 |

The 1,290 test series are **stratified across all eight trend × seasonality ×
forecastability (TSF) regime cells** (~160 series/cell), ensuring balanced
evaluation.

**Train/test split (upstream):** Global temporal cutoff at **2023-07-28 00:00:00
UTC**. The data published here is the **test set**.

## Source Schema

Each row represents one timestamp of one series (long/tidy format).

| Column | Type | Description |
|---|---|---|
| `item_id` | int64 | Unique series identifier |
| `date_time` | datetime64[ns] (UTC) | UTC timestamp |
| `ind_1` … `ind_5` | float64 | Five anonymised traffic variates |

## TsFile Layout

```
.
├── README.md
├── hour/
│   ├── test_hour_1.tsfile … test_hour_8.tsfile   (table: quitobench_hour)
└── min/
    ├── test_min_1.tsfile  … test_min_5.tsfile    (table: quitobench_min)
```

Each config is one TsFile **table**, written by the converter as several
shards (the tooling splits large outputs into multiple `.tsfile` parts). All
shards of a config carry the same table schema; read them together to get the
full table.

- **Table**: `quitobench_hour` / `quitobench_min`
- **TAG** (device): `item_id` — the source int64 id rendered as a STRING
  (TsFile tags must be STRING), e.g. `"100011"`. One series = one device.
- **Time**: `date_time` (UTC) → INT64 epoch **milliseconds**.
- **FIELD**: `ind_1`, `ind_2`, `ind_3`, `ind_4`, `ind_5` → DOUBLE (source float64).

No columns were dropped; both test splits have no nulls and no
`(item_id, date_time)` duplicates, and every series has a uniform length.

## Reading the TsFiles

```python
import glob
from tsfile import TsFileReader

# Iterate over all shards of one config and concatenate
total = 0
for shard in sorted(glob.glob("hour/*.tsfile")):
    reader = TsFileReader(shard)
    name = next(iter(reader.get_all_table_schemas()))          # "quitobench_hour"
    schema = reader.get_all_table_schemas()[name]
    # Include the TAG column (item_id) explicitly to get it back in the result.
    cols = [c.get_column_name() for c in schema.get_columns()]  # item_id, ind_1..5
    with reader.query_table(name, cols, batch_size=200_000) as rs:
        while (batch := rs.read_arrow_batch()) is not None:
            df = batch.to_pandas()   # columns: time, item_id, ind_1..ind_5
            total += len(df)
print(total)  # 7,939,052 for hour
```

> **Note on querying tags.** A TAG column (`item_id`) is a device-identifying
> dimension. To get it back in query results, include it explicitly in the
> column list. A query of only the tag (no FIELD column) returns 0 rows — this
> is expected TsFile table-model behavior, not a corrupt file.

## License & Citation

Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
Please preserve attribution to the original authors.

- Original dataset: [`hq-bench/quitobench`](https://huggingface.co/datasets/hq-bench/quitobench)

```bibtex
@article{xue2026quitobench,
  title   = {{QuitoBench}: A High-Quality Open Time Series Forecasting Benchmark},
  author  = {Xue, Siqiao and Zhu, Zhaoyang and Zhang, Wei and
             Cai, Rongyao and Wang, Rui and
             Mu, Yixiang and Zhou, Fan and Li, Jianguo and Di, Peng and Yu, Hang},
  journal = {arXiv preprint arXiv:2603.26017},
  year    = {2026},
  url     = {https://arxiv.org/abs/2603.26017}
}
```