Falcon-X introduces a latent prototype space with Unified Prototype Diff-Attention and Latent Entity Attention for heterogeneous multivariate time series forecasting.
Toto 2.0: Time Series Forecasting Enters the Scaling Era
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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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cs.LG 1years
2026 1verdicts
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Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
Falcon-X introduces a latent prototype space with Unified Prototype Diff-Attention and Latent Entity Attention for heterogeneous multivariate time series forecasting.