| title | Toto 2.0: Time Series Forecasting Enters the Scaling Era |
|---|---|
| authors | Emaad Khwaja, Chris Lettieri, Gerald Woo, Eden Belouadah, Marc Cenac, Guillaume Jarry, Enguerrand Paquin, Xunyi Zhao, Viktoriya Zhukov, Othmane Abou-Amal, Chenghao Liu, Ameet Talwalkar, David Asker |
| date | 2026-05-19 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.20119 |
| affiliation | Datadog |
| upvotes | 38 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | We show that time series foundation models scale: a single training recipe produces reliable forecast-quality improvements from 4M to 2.5B parameters. We release Toto 2.0, a family of five open-weights forecasting models trained under this recipe. The Toto 2.0 family sets a new state of the art on three forecasting benchmarks: BOOM, our observability benchmark; GIFT-Eval, the standard general-purpose benchmark; and the recent contamination-resistant TIME benchmark. This report describes our experimental results and details the design decisions behind Toto 2.0: its architecture and training recipe, training data, and the u-muP hyperparameter transfer pipeline. All five base checkpoints are released under Apache 2.0. |
cite
@misc{khwaja2026toto,
title={Toto 2.0: Time Series Forecasting Enters the Scaling Era},
author={Emaad Khwaja and Chris Lettieri and Gerald Woo and Eden Belouadah and Marc Cenac and Guillaume Jarry and Enguerrand Paquin and Xunyi Zhao and Viktoriya Zhukov and Othmane Abou-Amal and Chenghao Liu and Ameet Talwalkar and David Asker},
year={2026},
eprint={2605.20119},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.20119}
}