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

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

Overview

<note markdown=“1”> This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor, see AttributeConfig.

</note>

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.

See Also:

Constant Summary collapse

SENSITIVE = 
[]

Instance Attribute Summary collapse

Instance Attribute Details

#featurizations ⇒ Array<Types::Featurization>

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

Returns:



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# File 'lib/aws-sdk-forecastservice/types.rb', line 4240

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

#forecast_dimensions ⇒ Array<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 'lib/aws-sdk-forecastservice/types.rb', line 4240

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

#forecast_frequency ⇒ String

The frequency of predictions in a forecast.

Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, “1D” indicates every day and “15min” indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:

  • Minute - 1-59

  • Hour - 1-23

  • Day - 1-6

  • Week - 1-4

  • Month - 1-11

  • Year - 1

Thus, if you want every other week forecasts, specify “2W”. Or, if you want quarterly forecasts, you specify “3M”.

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 TARGET_TIME_SERIES dataset frequency.

Returns:

  • (String) 


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# File 'lib/aws-sdk-forecastservice/types.rb', line 4240

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