Checked fact 314029 Oct 2026Models and releases

The h2ofm page describes TimeH2O as time-series forecasting, supporting univariate and multi-target series and covariates, and returning point (median), quantile and mean forecasts.

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The exact words it rests on

a 2D array batches univariate series; a 3D array batches multi-target series.

What the source said when we opened it, on 29 Oct 2026.

The source

h2ofm: standalone inference for H2O.ai foundation models (TimeH2O)
H2O.ai (PyPI) · 2026-10-27

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Checked

Checked by the notis newsroom on 29 Oct 2026, against the source above.

In the story

H2O.ai's h2ofm package ships TimeH2O forecasting models of 108.8M and 5.8M parameters 29 Oct 2026

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In a sentence

“The h2ofm page describes TimeH2O as time-series forecasting, supporting univariate and multi-target series and covariates, and returning point (median), quantile and mean forecasts.” ai notis, checked 29 Oct 2026. Source: h2ofm: standalone inference for H2O.ai foundation models (TimeH2O) (H2O.ai (PyPI)). https://ainotis.com/c/3140

As a reference (APA style)

ai notis. (2026, October 29). The h2ofm page describes TimeH2O as time-series forecasting, supporting univariate and multi-target series and covariates, and returning point (median), quantile and mean forecasts [Checked claim 3140]. ai notis. https://ainotis.com/c/3140

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