class Aws::ForecastService::Types::FeaturizationConfig

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.

@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",
              },
            },
          ],
        },
      ],
    }

@!attribute [rw] forecast_frequency

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.
@return [String]

@!attribute [rw] forecast_dimensions

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.
@return [Array<String>]

@!attribute [rw] featurizations

An array of featurization (transformation) information for the
fields of a dataset.
@return [Array<Types::Featurization>]

@see docs.aws.amazon.com/goto/WebAPI/forecast-2018-06-26/FeaturizationConfig AWS API Documentation

Constants

SENSITIVE