TimeSeriesCloudPredictor.predict_real_time#

TimeSeriesCloudPredictor.predict_real_time(test_data: Union[str, DataFrame], id_column: str, timestamp_column: str, target: str, static_features: Optional[Union[DataFrame, str]] = None, accept: str = 'application/x-parquet') → 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.

Parameters

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

  • id_column (str) – Name of the ‘item_id’ column

  • timestamp_column (str) – Name of the ‘timestamp’ column

  • 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.predictor.html#timeseriesdataframe

  • target (str) – Name of column that contains the target values to forecast

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

Returns

  • Pandas.DataFrame

  • Predict results in DataFrame