predict

TimeSeriesCloudPredictor.predict(data: str | DataFrame, static_features: str | DataFrame | None = None, known_covariates: str | DataFrame | None = None, predictor_path: str | None = None, framework_version: str = 'latest', job_name: str | None = None, instance_type: str = 'ml.m5.2xlarge', instance_count: int = 1, image_uri: str | None = None, wait: bool = True, download: bool = True, persist: bool = True, save_path: str | None = None, environment: Dict[str, str] | None = None, sagemaker_overrides: Dict[str, Dict[str, Any]] | None = None) → DataFrame | None[source]

Predict using SageMaker batch transform. When minimizing latency isn’t a concern, then the batch transform functionality may be easier, more scalable, and more appropriate. If you want to minimize latency, use predict_real_time() instead. To learn more: https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html

data must use the same id_column / timestamp_column names that were passed to fit().

Parameters:

  • data (Union(str, pandas.DataFrame)) – Historical time series to forecast from, in long format, as a DataFrame or local/S3 path to a data file.

  • static_features (Optional[Union[str, pd.DataFrame]]) – Static (time-independent) features describing each individual time series.

  • known_covariates (Optional[Union[str, pd.DataFrame]]) – Future values of the known covariates over the forecast horizon. Must be provided if known_covariates_names was specified at fit time.

  • predictor_path (str) – Path to the predictor tarball you want to use to predict. Path can be both a local path or a S3 location. If None, will use the most recent trained predictor trained with fit().

  • framework_version (str, default = latest) – Inference container version of autogluon. If latest, will use the latest available container version. If provided a specific version, will use this version. If image_uri is set, this argument will be ignored.

  • job_name (str, default = None) – Name of the launched training job. If None, CloudPredictor creates one with prefix ag-cloud-timeseries.

  • instance_count (int, default = 1,) – Number of instances used to do batch transform.

  • instance_type (str, default = 'ml.m5.2xlarge') – Instance to be used for batch transform.

  • wait (bool, default = True) – Whether to wait for batch transform to complete. To be noticed, the function won’t return immediately because there are some preparations needed prior transform.

  • image_uri (Optional[str], default = None) – Custom inference container image. If set, framework_version is ignored.

  • download – Same as in TabularCloudPredictor.predict().

  • persist – Same as in TabularCloudPredictor.predict().

  • save_path – Same as in TabularCloudPredictor.predict().

  • environment – Same as in TabularCloudPredictor.predict().

  • sagemaker_overrides – Same as in TabularCloudPredictor.predict().