QuitoBench: A High-Quality Open Time Series Forecasting Benchmark
Time series forecasting is critical across finance, healthcare, and cloud computing, yet progress is constrained by a fundamental bottleneck: the scarcity of large-scale, high-quality benchmarks. To address this gap, we introduce \textsc{QuitoBench}, a regime-balanced benchmark for time series forecasting with coverage across eight trend$\times$seasonality$\times$forecastability (TSF) regimes, designed to capture forecasting-relevant properties rather than application-defined domain labels. The benchmark is built upon \textsc{Quito}, a billion-scale time series corpus of application traffic from Alipay spanning nine business domains. Benchmarking 10 models from deep learning, foundation m...
| Authors | Siqiao Xue, Zhaoyang Zhu, Wei Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
Models and datasets referencing this paper
- THULab/quitobenchDataset
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