TimeSeriesCloudPredictor.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.

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

Parameters:

  • data (Union(str, pandas.DataFrame)) – The data to forecast from. Can be a pandas.DataFrame or a local path to a csv file.

  • static_features (Optional[pd.DataFrame]) – An optional data frame describing the metadata attributes of individual items in the item index. For more detail, please refer to TimeSeriesDataFrame documentation: https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesDataFrame.html

  • known_covariates (Optional[pd.DataFrame]) – If known_covariates_names were specified when creating the predictor, it is necessary to provide the values of the known covariates for each time series during the forecast horizon. For more details, please refer to the TimeSeriesPredictor.predictor documentation: https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesPredictor.predict.html

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

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

Returns:

  • Pandas.DataFrame

  • Predict results in DataFrame