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Toto 2.0 models show scaling benefits in time series forecasting

By PulseAugur Editorial · [1 sources] ·

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

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

RANK_REASON 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]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Toto 2.0 models show scaling benefits in time series forecasting

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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]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · David Asker ·

    Toto 2.0: Time Series Forecasting Enters the Scaling Era

    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 s…

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