Class: Aws::ForecastService::Types::FeaturizationConfig

Inherits:
Struct
  • Object
show all
Defined in:
gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb

Overview

Note:

When making an API call, you may pass FeaturizationConfig data as a hash:

{
  forecast_frequency: "Frequency", # required
  forecast_dimensions: ["Name"],
  featurizations: [
    {
      attribute_name: "Name", # required
      featurization_pipeline: [
        {
          featurization_method_name: "filling", # required, accepts filling
          featurization_method_parameters: {
            "ParameterKey" => "ParameterValue",
          },
        },
      ],
    },
  ],
}

In a CreatePredictor operation, the specified algorithm trains a model using the specified dataset group. You can optionally tell the operation to modify data fields prior to training a model. These modifications are referred to as featurization.

You define featurization using the FeaturizationConfig object. You specify an array of transformations, one for each field that you want to featurize. You then include the FeaturizationConfig object in your CreatePredictor request. Amazon Forecast applies the featurization to the TARGET_TIME_SERIES and RELATED_TIME_SERIES datasets before model training.

You can create multiple featurization configurations. For example, you might call the CreatePredictor operation twice by specifying different featurization configurations.

Constant Summary collapse

SENSITIVE =
[]

Instance Attribute Summary collapse

Instance Attribute Details

#featurizationsArray<Types::Featurization>

An array of featurization (transformation) information for the fields of a dataset.

Returns:



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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2082

class FeaturizationConfig < Struct.new(
  :forecast_frequency,
  :forecast_dimensions,
  :featurizations)
  SENSITIVE = []
  include Aws::Structure
end

#forecast_dimensionsArray<String>

An array of dimension (field) names that specify how to group the generated forecast.

For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.

All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.

Returns:

  • (Array<String>)


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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2082

class FeaturizationConfig < Struct.new(
  :forecast_frequency,
  :forecast_dimensions,
  :featurizations)
  SENSITIVE = []
  include Aws::Structure
end

#forecast_frequencyString

The frequency of predictions in a forecast.

Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "Y" indicates every year and "5min" indicates every five minutes.

The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.

When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the RELATED_TIME_SERIES dataset frequency.

Returns:

  • (String)


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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 2082

class FeaturizationConfig < Struct.new(
  :forecast_frequency,
  :forecast_dimensions,
  :featurizations)
  SENSITIVE = []
  include Aws::Structure
end