predict_real_time

TimeSeriesCloudPredictor.predict_real_time(data: str | DataFrame, static_features: str | DataFrame | None = None, known_covariates: DataFrame | None = None, accept: str = 'application/x-parquet', **kwargs) → DataFrame[source]

Predict with the deployed SageMaker endpoint. A deployed SageMaker endpoint is required. This is intended to provide a low latency inference. If you want to inference on a large dataset, use predict() instead.

Deprecated since version Use: predict() of the endpoint returned by deploy() instead.

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

Parameters:

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

  • static_features (pd.DataFrame | None) – Static (time-independent) features describing each individual time series.

  • known_covariates (pd.DataFrame | None) – Future values of the known covariates over the forecast horizon. Must be provided if known_covariates_names was specified at fit time.

  • accept (str, default = application/x-parquet) – Type of accept output content. Valid options are application/x-parquet, text/csv, application/json

  • **kwargs (Any) – Additional args that you would pass to predict calls of an AutoGluon logic

Returns:

pd.DataFrame – Predict results in pd.DataFrame

SageMaker API

  • InvokeEndpoint: sends the data to the endpoint and returns the predictions. The payload is limited to 6 MB (4 MB for serverless endpoints).