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

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing FeaturizationConfig as input to an Aws::Client method, you can use a vanilla 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 in your CreatePredictor request. Amazon Forecast applies the featurization to the TARGET_TIME_SERIES dataset before model training.

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

Returned by:

Instance Attribute Summary collapse

Instance Attribute Details

#featurizationsArray<Types::Featurization>

An array of featurization (transformation) information for the fields of a dataset. In this release, only a single featurization is supported.

Returns:

  • (Array<Types::Featurization>)

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

#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.

Returns:

  • (Array<String>)

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

#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.

Returns:

  • (String)

    The frequency of predictions in a forecast.