NAME

Paws::Forecast::FeaturizationConfig

USAGE

This class represents one of two things:

Arguments in a call to a service

Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. Each attribute should be used as a named argument in the calls that expect this type of object.

As an example, if Att1 is expected to be a Paws::Forecast::FeaturizationConfig object:

  $service_obj->Method(Att1 => { Featurizations => $value, ..., ForecastFrequency => $value  });

Results returned from an API call

Use accessors for each attribute. If Att1 is expected to be an Paws::Forecast::FeaturizationConfig object:

  $result = $service_obj->Method(...);
  $result->Att1->Featurizations

DESCRIPTION

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.

ATTRIBUTES

Featurizations => ArrayRef[Paws::Forecast::Featurization]

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

ForecastDimensions => ArrayRef[Str|Undef]

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.

REQUIRED ForecastFrequency => Str

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.

BUGS and CONTRIBUTIONS

The source code is located here: <https://github.com/pplu/aws-sdk-perl>

Please report bugs to: <https://github.com/pplu/aws-sdk-perl/issues>