Datasets:
Tasks:
Time Series Forecasting
Modalities:
Time-series
Languages:
English
Size:
10M<n<100M
ArXiv:
License:
| 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} | |
| } | |
| ``` | |