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.

翻译：我们证明时间序列基础模型具有可扩展性：单一训练方案可在参数量从4M到2.5B范围内实现可靠的预测质量提升。我们发布Toto 2.0——遵循该方案训练的五款开源权重预测模型系列。Toto 2.0系列在三个预测基准上创下新最优结果：观测性基准BOOM、标准通用基准GIFT-Eval以及近期推出的抗污染基准TIME。本报告阐述我们的实验结果并详述Toto 2.0背后的设计决策：其架构与训练方案、训练数据，以及u-muP超参数迁移管道。所有五个基础检查点均依据Apache 2.0协议发布。