README.md

metadata

license: cc-by-4.0
task_categories:
  - time-series-forecasting
language:
  - en
tags:
  - time-series
  - forecasting
  - application-traffic
  - cloud-computing
  - benchmark
  - TSF-regime
  - regime-balanced
  - single-provenance
pretty_name: 'QuitoBench: A High-Quality Open Time Series Forecasting Benchmark'
size_categories:
  - 10M<n<100M
configs:
  - config_name: hour
    data_files:
      - split: test
        path: v20260315/test_hour-00001-of-00001.parquet
    description: >
      Hourly evaluation split (1-hour granularity). 517 test series, each with
      15,356 time steps spanning 2021-11-18 to 2023-08-19. Test-set length per
      series: 552 steps.
  - config_name: min
    data_files:
      - split: test
        path: v20260315/test_min-00001-of-00001.parquet
    description: >
      10-minute evaluation split (10-min granularity). 773 test series, each
      with 5,904 time steps spanning 2023-07-10 to 2023-08-19. Test-set length
      per series: 3,312 steps.

QuitoBench

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 📄 Paper: arXiv:2603.26017 💻 Code: github.com/alipay/quito 📦 Training Corpus: 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

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: Global temporal cutoff at 2023-07-28 00:00:00 UTC. Data before the cutoff is train (70%) / validation (20%); data from the cutoff onward is the test set.

---

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, sort by date_time, then apply the 2023-07-28 cutoff for train/test splits.

---

Quick Start

from datasets import load_dataset

# Load hourly test split
ds_hour = load_dataset("hq-bench/quitobench", "hour")
df_hour = ds_hour["test"].to_pandas()

# Load 10-minute test split
ds_min = load_dataset("hq-bench/quitobench", "min")
df_min = ds_min["test"].to_pandas()

Reconstruct train/test splits

import pandas as pd

CUTOFF = pd.Timestamp("2023-07-28 00:00:00")
df = load_dataset("hq-bench/quitobench", "hour")["test"].to_pandas()

# Pick one series
series = df[df["item_id"] == df["item_id"].iloc[0]].sort_values("date_time")

train = series[series["date_time"] < CUTOFF]
test  = series[series["date_time"] >= CUTOFF]

X_train = train[["ind_1", "ind_2", "ind_3", "ind_4", "ind_5"]].values
X_test  = test[["ind_1", "ind_2", "ind_3", "ind_4", "ind_5"]].values

---

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}
}