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

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

  • known_covariates (Optional[pd.DataFrame]) – 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 – Additional args that you would pass to predict calls of an AutoGluon logic

Returns:

  • Pandas.DataFrame

  • Predict results in DataFrame