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Moirai-1.0-R-Large

Model directory
Salesforce/moirai-1.0-R-large

311M params | Upstream checkpoint

Moirai-1.0-R-Large is the highest-capacity dense checkpoint in Salesforce's original Moirai family, the top tier above Moirai-1.0-R-Small and Moirai-1.0-R-Base. At 311M parameters it is the first-generation Moirai design at its largest published scale, aimed at the strongest broad zero-shot performance the dense 1.0-R line offers.

It preserves the same masked-encoder transformer as the smaller checkpoints, with multi-patch projections, any-variate attention, and a mixture-distribution output head. The any-variate attention handles arbitrary numbers of target variables and dynamic covariates in one model, and the mixture output head yields probabilistic forecasts with calibrated quantiles. Like the rest of the family it is pretrained on the LOTSA corpus, the large open time-series archive behind the Moirai work, which underpins its zero-shot generalization.

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Model Classification

Family

Moirai-1.0-R

Type

time series foundation model

Pretrained time-series model for zero-shot or few-shot forecasting workloads.

Training Data

LOTSA, the Large-scale Open Time Series Archive, with roughly 27B observations across nine domains including energy, transport, finance, healthcare, sales, climate, web, and social data.

Recommended For

  • • Multivariate forecasting across heterogeneous domains
  • • Workloads that benefit from probabilistic outputs and arbitrary variate counts

Strengths

  • • Strong multivariate coverage across the Moirai family
  • • Well-suited to covariates and correlated series

Limitations

  • • Model cards for some newer Moirai variants are still sparse on exact checkpoint details
  • • Heavier family choices can be more expensive than tiny single-purpose baselines

Capabilities

forecastingquantile-forecastingmultivariatecovariateszero-shot

Tags

salesforcemoiraimultivariatequality-tier

Specifications

Parameters
311M
Architecture
masked encoder transformer with multi-patch projections, any-variate attention, and mixture-distribution output
Context length
n/a
Max context
n/a
Minimum history
n/a
Recommended history
n/a
Input step
n/a
Required target series
1
Temperature
Ignored
Top P
Ignored
Max output
n/a
License
n/a

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