Toto 2.0 models show scaling benefits in time series forecasting

Researchers have introduced Toto 2.0, a suite of five open-weight time series forecasting models that demonstrate the effectiveness of scaling foundation models. The models, trained using a single recipe, show improved forecast quality as their parameter count increases from 4 million to 2.5 billion. Toto 2.0 has achieved state-of-the-art results on three key forecasting benchmarks: BOOM, GIFT-Eval, and TIME. AI

影响 Demonstrates that scaling laws apply to time series forecasting, potentially improving accuracy across various applications.

排序理由 Release of an open-source model family with benchmark results described in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• BOOM
• GIFT-Eval
• TIME
• Toto 2.0

• 模型发布
• 论文
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