hq-bench/quito-corpus

Quito

Quito is a billion-scale, single-provenance time series dataset of application-traffic workloads collected from Alipay's production platform, spanning nine business verticals from finance and e-commerce to infrastructure and IoT.

🌐 Project Page: hq-bench.github.io/quito 📄 Paper: arXiv:2603.26017 💻 Code: github.com/alipay/quito 📊 Benchmark Set: hq-bench/quitobench

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Dataset Overview

	hour config	min config
Granularity	1 hour	10 minutes
# Series	12,544	22,522
Series length	15,356 steps	5,904 steps
Date range	2021-11-18 → 2023-08-19	2023-07-10 → 2023-08-19
# Variates / series	5	5
Total tokens	1.0 Billion	0.7 Billion

The two subsets are drawn from disjoint pools of applications (no overlap in item_ids). The differing start dates reflect the production system's tiered retention policy: hourly aggregates are archived long-term, while 10-minute telemetry is retained for a shorter rolling window.

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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 timestamp
ind_1 … ind_5	float64	Five anonymised traffic variates (NaN for missing)

To reconstruct a single multivariate series: filter by item_id and sort by date_time.

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Quick Start

from datasets import load_dataset

# Load hourly training corpus
ds_hour = load_dataset("hq-bench/quito-corpus", "hour")
df_hour = ds_hour["train"].to_pandas()

# Load 10-minute training corpus
ds_min = load_dataset("hq-bench/quito-corpus", "min")
df_min = ds_min["train"].to_pandas()

Iterate over individual series

for item_id, series_df in df.groupby("item_id"):
    series_df = series_df.sort_values("date_time")
    # series_df has columns: date_time, ind_1 … ind_5
    break  # remove to iterate all series

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License

CC BY 4.0

Citation

@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}
}