Toto 2.0: Time Series Forecasting Enters the Scaling Era

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

arXiv · 2605.20119

Toto 2.0: Time Series Forecasting Enters the Scaling Era

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

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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.

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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. 2026-06-04. Toto 2.0: Time Series Forecasting Enters the Scaling Era. https://arxiv.org/abs/2605.20119

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