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

cs.LGMay 19, 2026

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, +5 more

Organizations: Datadog AI Research · Carnegie Mellon University

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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Sep 21, 2026 · cs.LGJ/K move · Enter open · S save

$t_0$ : A Time-Series Foundation Model for Forecasting with Context

Lucas Meyer, Claudio Sole, Huikan Xiang, Nicolas Li+5

We present

t_0

, a family of open-weights foundation models for forecasting with multivariate context. We release its first two members:

\texttt{t0-alpha}

and

\texttt{t0-beta}

, respectively 102M and 256M parameters. Both condition their forecasts on target history, past covariates, and known-future covariates, without task-specific retraining. Their transformer layers alternate attention along time and across variates. They produce probabilistic forecasts through quantile predictions. Pretraining combines curated public data with synthetic generator families constructed to contain covariate-to-target dependencies. On GIFT-Eval,

\texttt{t0-alpha}

reaches an aggregate CRPS of 0.4941, and

\texttt{t0-beta}

a CRPS of 0.4738 and a MASE of 0.6865, third on both and within 4.0% of the best zero-shot TSFM. On fev-bench they score 42.2 and 46.7 in skill, the latter third again and 2.0 points behind the leader. We analyze

\texttt{t0-alpha}

in depth. Known-future covariates raise its skill by 6.3 percentage points across 30 tasks. The report also examines its calibration, its rollout strategy on long horizons, and its robustness to missing data. On the Victoria electricity-demand benchmark,

\texttt{t0-beta}

is among the most accurate models with a context of nearly a year. In an independent Macrocosm evaluation of hourly ERCOT prices over 29 months, both cut the MAE of the lagged-price baseline by 38%.

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Abstract

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