Toto 2.0: Time Series Forecasting Enters the Scaling Era
Authors: name: Emaad Khwaja, name: Chris Lettieri, name: Gerald Woo, name: Eden Belouadah, name: Marc Cenac, name: Guillaume Jarry, name: Enguerrand Paquin, name: Xunyi Zhao, name: Viktoriya Zhukov, name: Othmane Abou-Amal, name: Chenghao Liu, name: Ameet Talwalkar, name: David Asker
Organization: Datadog

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.