#[non_exhaustive]
pub struct AttributeConfig { pub attribute_name: Option<String>, pub transformations: Option<HashMap<String, String>>, }
Expand description

Provides information about the method used to transform attributes.

The following is an example using the RETAIL domain:

{

"AttributeName": "demand",

"Transformations": {"aggregation": "sum", "middlefill": "zero", "backfill": "zero"}

}

Fields (Non-exhaustive)

This struct is marked as non-exhaustive
Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.
attribute_name: Option<String>

The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target time series and the related time series datasets. For example, for the RETAIL domain, the target is demand.

transformations: Option<HashMap<String, String>>

The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to override the default values. Related Time Series attributes do not accept aggregation parameters.

The following list shows the parameters and their valid values for the "filling" featurization method for a Target Time Series dataset. Default values are bolded.

  • aggregation: sum, avg, first, min, max

  • frontfill: none

  • middlefill: zero, nan (not a number), value, median, mean, min, max

  • backfill: zero, nan, value, median, mean, min, max

The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):

  • middlefill: zero, value, median, mean, min, max

  • backfill: zero, value, median, mean, min, max

  • futurefill: zero, value, median, mean, min, max

To set a filling method to a specific value, set the fill parameter to value and define the value in a corresponding _value parameter. For example, to set backfilling to a value of 2, include the following: "backfill": "value" and "backfill_value":"2".

Implementations

The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target time series and the related time series datasets. For example, for the RETAIL domain, the target is demand.

The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to override the default values. Related Time Series attributes do not accept aggregation parameters.

The following list shows the parameters and their valid values for the "filling" featurization method for a Target Time Series dataset. Default values are bolded.

  • aggregation: sum, avg, first, min, max

  • frontfill: none

  • middlefill: zero, nan (not a number), value, median, mean, min, max

  • backfill: zero, nan, value, median, mean, min, max

The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):

  • middlefill: zero, value, median, mean, min, max

  • backfill: zero, value, median, mean, min, max

  • futurefill: zero, value, median, mean, min, max

To set a filling method to a specific value, set the fill parameter to value and define the value in a corresponding _value parameter. For example, to set backfilling to a value of 2, include the following: "backfill": "value" and "backfill_value":"2".

Creates a new builder-style object to manufacture AttributeConfig

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