autogluon.cloud.TimeSeriesFoundationModel

class autogluon.cloud.TimeSeriesFoundationModel(model_id: str, **kwargs)[source]

Foundation model for time series forecasting (Chronos, etc.).

__init__(model_id: str, backend: Literal['sagemaker'] = 'sagemaker', cloud_output_path: str | None = None, hyperparameters: Dict[str, Any] | None = None, role: str | None = None)
Parameters:

  • model_id – ID of the foundation model from the model registry.

  • backend – Cloud backend to use.

  • cloud_output_path – S3 path to store intermediate artifacts.

  • hyperparameters – Default hyperparameters applied to inference and (when supported) training.

  • role – ARN of the SageMaker execution role used to run training and inference jobs. If None, falls back to role_arn in ~/.autogluon/cloud.yaml (set by autogluon.cloud.bootstrap() / autogluon.cloud.register()), and finally to sagemaker.get_execution_role().

Methods

deploy

Deploy model to a real-time endpoint.

predict

Run batch prediction for time series.