<note> <p>Explainability is only available for Forecasts and Predictors generated from an AutoPredictor (<a>CreateAutoPredictor</a>)</p> </note> <p>Creates an Amazon Forecast Explainability.</p> <p>Explainability helps you better understand how the attributes in your datasets impact forecast. Amazon Forecast uses a metric called Impact scores to quantify the relative impact of each attribute and determine whether they increase or decrease forecast values.</p> <p>To enable Forecast Explainability, your predictor must include at least one of the following: related time series, item metadata, or additional datasets like Holidays and the Weather Index.</p> <p>CreateExplainability accepts either a Predictor ARN or Forecast ARN. To receive aggregated Impact scores for all time series and time points in your datasets, provide a Predictor ARN. To receive Impact scores for specific time series and time points, provide a Forecast ARN.</p> <p> <b>CreateExplainability with a Predictor ARN</b> </p> <note> <p>You can only have one Explainability resource per predictor. If you already enabled <code>ExplainPredictor</code> in <a>CreateAutoPredictor</a>, that predictor already has an Explainability resource.</p> </note> <p>The following parameters are required when providing a Predictor ARN:</p> <ul> <li> <p> <code>ExplainabilityName</code> - A unique name for the Explainability.</p> </li> <li> <p> <code>ResourceArn</code> - The Arn of the predictor.</p> </li> <li> <p> <code>TimePointGranularity</code> - Must be set to “ALL”.</p> </li> <li> <p> <code>TimeSeriesGranularity</code> - Must be set to “ALL”.</p> </li> </ul> <p>Do not specify a value for the following parameters:</p> <ul> <li> <p> <code>DataSource</code> - Only valid when TimeSeriesGranularity is “SPECIFIC”.</p> </li> <li> <p> <code>Schema</code> - Only valid when TimeSeriesGranularity is “SPECIFIC”.</p> </li> <li> <p> <code>StartDateTime</code> - Only valid when TimePointGranularity is “SPECIFIC”.</p> </li> <li> <p> <code>EndDateTime</code> - Only valid when TimePointGranularity is “SPECIFIC”.</p> </li> </ul> <p> <b>CreateExplainability with a Forecast ARN</b> </p> <note> <p>You can specify a maximum of 50 time series and 500 time points.</p> </note> <p>The following parameters are required when providing a Predictor ARN:</p> <ul> <li> <p> <code>ExplainabilityName</code> - A unique name for the Explainability.</p> </li> <li> <p> <code>ResourceArn</code> - The Arn of the forecast.</p> </li> <li> <p> <code>TimePointGranularity</code> - Either “ALL” or “SPECIFIC”.</p> </li> <li> <p> <code>TimeSeriesGranularity</code> - Either “ALL” or “SPECIFIC”.</p> </li> </ul> <p>If you set TimeSeriesGranularity to “SPECIFIC”, you must also provide the following:</p> <ul> <li> <p> <code>DataSource</code> - The S3 location of the CSV file specifying your time series.</p> </li> <li> <p> <code>Schema</code> - The Schema defines the attributes and attribute types listed in the Data Source.</p> </li> </ul> <p>If you set TimePointGranularity to “SPECIFIC”, you must also provide the following:</p> <ul> <li> <p> <code>StartDateTime</code> - The first timestamp in the range of time points.</p> </li> <li> <p> <code>EndDateTime</code> - The last timestamp in the range of time points.</p> </li> </ul>

{
  "success": true,
  "data": {
    "id": "abc123",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

{
  "success": false,
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "Invalid request parameters"
  }
}