Toto 2.0: Time Series Forecasting Enters the Scaling Era
This work provides strong evidence that scaling laws apply to time series forecasting, benefiting practitioners who need accurate forecasts across diverse domains.
The authors demonstrate that time series foundation models scale reliably from 4M to 2.5B parameters, achieving state-of-the-art results on three benchmarks (BOOM, GIFT-Eval, TIME). They release the Toto 2.0 family of five open-weights models.
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.