H2O.ai, the trusted Sovereign AI Platform with the software and expertise to run predictive, generative, and agentic AI in production, today released TimeH2O-v1, a time-series foundation model that ranks first of 32 models on the fev-bench leaderboard by win rate. TimeH2O-v1 posted a 91.9% win rate and a skill score of 50.8 across the benchmark’s 100 forecasting tasks, ahead of Timestral-209M, t0-gamma, TimesFM-3, and Chronos-2.
Forecasts run the business: demand, inventory, cash, energy load, patient volume. Most organizations still build and maintain a separate model for each of those series, and retrain it when the data shifts. A forecasting foundation model works zero-shot: it reads a series’ history and forecasts it with no training on that data. TimeH2O-v1 was never trained on any fev-bench dataset, and it still ranks first.
“Building your business with accurate forecasting and prediction is the superpower of AI. TimeH2O gives organizations a generalized, pre-trained model for time series that can then be fine-tuned into a domain-specific foundation model for each customer’s use case,” said Sri Ambati, Founder and CEO of H2O.ai.
How TimeH2O-v1 performs
fev-bench scores models zero-shot on real time series across many domains and frequencies, measuring probabilistic accuracy against a seasonal-naive baseline. As of October 23, 2026, on the leaderboard’s default view:
- First by win rate. TimeH2O-v1 beats the other models on 91.9% of head-to-head comparisons, ranking first of 32. Timestral-209M follows at 89.6% and t0-gamma at 88.1%.
- A skill score of 50.8. Its forecast error is 50.8% lower than the seasonal-naive baseline’s, aggregated across all 100 tasks.
- Zero-shot. At forecast time, the model reads each series’ history and predicts from it. No training or fine-tuning happens: its weights never change.
TimeH2O-v1’s full results, and H2O.ai’s standings on other industry benchmarks, are at h2o.ai/benchmarks.
Built for business data
TimeH2O-v1 has 108 million parameters. It forecasts univariate series and series with covariates, the outside signals such as price, promotions, or weather that move the number being forecast. It performs well on time series from finance, healthcare, supply chain, energy, and more.
Zero-shot is the starting point. Organizations can fine-tune TimeH2O-v1 on their own data for more accuracy in their domain, on their own infrastructure.
Availability
TimeH2O-v1 is available today on Hugging Face at huggingface.co/h2oai/timeh2o-v1, under a non-commercial license that allows internal benchmarking and testing. Organizations with commercial forecasting use cases can contact H2O.ai.
About H2O.ai
H2O.ai created the industry’s trusted Sovereign AI Platform because organizations should own their AI: their agents, models, data, and results, run anywhere from cloud to air-gapped. Super Agent orchestrates predictive, generative, and agentic AI in one system, with observability and governance, empowering regulated and public sector organizations to get value from AI. Moving from pilot to production takes expertise, from top-ranked data scientists, including Kaggle Grandmasters, to forward-deployed engineers embedded with customer teams. Built on a decade of open source, H2O.ai is trusted by 20,000+ organizations, half the Fortune 500. AT&T, Commonwealth Bank of Australia, Wells Fargo, Bank of America, Workday, Progressive Insurance, and NIH rely on H2O.ai to cut cost and risk and own their AI future. Through its AI for Good program, H2O.ai applies technology and investment to non-profits and academia solving hard problems in health, education, and the environment. Learn more at h2o.ai.
View source version on businesswire.com: https://www.businesswire.com/news/home/20261026408387/en/
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