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ExplainabilityConfig

Used by
Source code in docs_src/capo_forecast/types/explainability_config.py 
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class ExplainabilityConfig(TypedDict, closed=True):
    time_series_granularity: (
        "capo_forecast.types.time_series_granularity.TimeSeriesGranularity"
    )
    """<p>To create an Explainability for all time series in your datasets, use <code>ALL</code>. To create an Explainability for specific time series in your datasets, use <code>SPECIFIC</code>.</p> <p>Specify time series by uploading a CSV or Parquet file to an Amazon S3 bucket and set the location within the <a>DataDestination</a> data type.</p>"""
    time_point_granularity: (
        "capo_forecast.types.time_point_granularity.TimePointGranularity"
    )
    """<p>To create an Explainability for all time points in your forecast horizon, use <code>ALL</code>. To create an Explainability for specific time points in your forecast horizon, use <code>SPECIFIC</code>.</p> <p>Specify time points with the <code>StartDateTime</code> and <code>EndDateTime</code> parameters within the <a>CreateExplainability</a> operation.</p>"""

time_point_granularity instance-attribute 

time_point_granularity: TimePointGranularity

To create an Explainability for all time points in your forecast horizon, use ALL. To create an Explainability for specific time points in your forecast horizon, use SPECIFIC.

Specify time points with the StartDateTime and EndDateTime parameters within the CreateExplainability operation.

time_series_granularity instance-attribute 

time_series_granularity: TimeSeriesGranularity

To create an Explainability for all time series in your datasets, use ALL. To create an Explainability for specific time series in your datasets, use SPECIFIC.

Specify time series by uploading a CSV or Parquet file to an Amazon S3 bucket and set the location within the DataDestination data type.