NAME

Paws::Forecast::CreatePredictor - Arguments for method CreatePredictor on Paws::Forecast

DESCRIPTION

This class represents the parameters used for calling the method CreatePredictor on the Amazon Forecast Service service. Use the attributes of this class as arguments to method CreatePredictor.

You shouldn't make instances of this class. Each attribute should be used as a named argument in the call to CreatePredictor.

SYNOPSIS

    my $forecast = Paws->service('Forecast');
    my $CreatePredictorResponse = $forecast->CreatePredictor(
      FeaturizationConfig => {
        ForecastFrequency => 'MyFrequency',
        Featurizations    => [
          {
            AttributeName         => 'MyName',    # min: 1, max: 63
            FeaturizationPipeline => [
              {
                FeaturizationMethodName       => 'filling',    # values: filling
                FeaturizationMethodParameters => {
                  'MyParameterKey' =>
                    'MyParameterValue',    # key: max: 256, value: max: 256
                },    # min: 1, max: 20; OPTIONAL
              },
              ...
            ],        # min: 1, max: 1; OPTIONAL
          },
          ...
        ],            # min: 1, max: 1; OPTIONAL
        ForecastDimensions => [
          'MyName', ...    # min: 1, max: 63
        ],                 # min: 1, max: 5; OPTIONAL
      },
      ForecastHorizon => 1,
      InputDataConfig => {
        DatasetGroupArn       => 'MyArn',    # max: 256
        SupplementaryFeatures => [
          {
            Name  => 'MyName',               # min: 1, max: 63
            Value => 'MyValue',              # max: 256

          },
          ...
        ],                                   # min: 1, max: 1; OPTIONAL
      },
      PredictorName    => 'MyName',
      AlgorithmArn     => 'MyArn',           # OPTIONAL
      EncryptionConfig => {
        KMSKeyArn => 'MyKMSKeyArn',          # max: 256
        RoleArn   => 'MyArn',                # max: 256

      },    # OPTIONAL
      EvaluationParameters => {
        BackTestWindowOffset    => 1,
        NumberOfBacktestWindows => 1,
      },    # OPTIONAL
      HPOConfig => {
        ParameterRanges => {
          CategoricalParameterRanges => [
            {
              Name   => 'MyName',    # min: 1, max: 63
              Values => [
                'MyValue', ...       # max: 256
              ],                     # min: 1, max: 20

            },
            ...
          ],                         # min: 1, max: 20; OPTIONAL
          ContinuousParameterRanges => [
            {
              MaxValue    => 1,
              MinValue    => 1,
              Name        => 'MyName',    # min: 1, max: 63
              ScalingType => 'Auto'
              , # values: Auto, Linear, Logarithmic, ReverseLogarithmic; OPTIONAL
            },
            ...
          ],    # min: 1, max: 20; OPTIONAL
          IntegerParameterRanges => [
            {
              MaxValue    => 1,
              MinValue    => 1,
              Name        => 'MyName',    # min: 1, max: 63
              ScalingType => 'Auto'
              , # values: Auto, Linear, Logarithmic, ReverseLogarithmic; OPTIONAL
            },
            ...
          ],    # min: 1, max: 20; OPTIONAL
        },    # OPTIONAL
      },    # OPTIONAL
      PerformAutoML      => 1,    # OPTIONAL
      PerformHPO         => 1,    # OPTIONAL
      TrainingParameters => {
        'MyParameterKey' => 'MyParameterValue', # key: max: 256, value: max: 256
      },    # OPTIONAL
    );

    # Results:
    my $PredictorArn = $CreatePredictorResponse->PredictorArn;

    # Returns a L<Paws::Forecast::CreatePredictorResponse> object.

Values for attributes that are native types (Int, String, Float, etc) can passed as-is (scalar values). Values for complex Types (objects) can be passed as a HashRef. The keys and values of the hashref will be used to instance the underlying object. For the AWS API documentation, see https://docs.aws.amazon.com/goto/WebAPI/forecast/CreatePredictor

ATTRIBUTES

AlgorithmArn => Str

The Amazon Resource Name (ARN) of the algorithm to use for model training. Required if PerformAutoML is not set to true.

Supported algorithms:

• arn:aws:forecast:::algorithm/ARIMA

• arn:aws:forecast:::algorithm/Deep_AR_Plus

  Supports hyperparameter optimization (HPO)

• arn:aws:forecast:::algorithm/ETS

• arn:aws:forecast:::algorithm/NPTS

• arn:aws:forecast:::algorithm/Prophet

EncryptionConfig => Paws::Forecast::EncryptionConfig

An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

EvaluationParameters => Paws::Forecast::EvaluationParameters

Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.

REQUIRED FeaturizationConfig => Paws::Forecast::FeaturizationConfig

The featurization configuration.

REQUIRED ForecastHorizon => Int

Specifies the number of time-steps that the model is trained to predict. The forecast horizon is also called the prediction length.

For example, if you configure a dataset for daily data collection (using the DataFrequency parameter of the CreateDataset operation) and set the forecast horizon to 10, the model returns predictions for 10 days.

The maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.

HPOConfig => Paws::Forecast::HyperParameterTuningJobConfig

Provides hyperparameter override values for the algorithm. If you don't provide this parameter, Amazon Forecast uses default values. The individual algorithms specify which hyperparameters support hyperparameter optimization (HPO). For more information, see aws-forecast-choosing-recipes.

If you included the HPOConfig object, you must set PerformHPO to true.

REQUIRED InputDataConfig => Paws::Forecast::InputDataConfig

Describes the dataset group that contains the data to use to train the predictor.

PerformAutoML => Bool

Whether to perform AutoML. When Amazon Forecast performs AutoML, it evaluates the algorithms it provides and chooses the best algorithm and configuration for your training dataset.

The default value is false. In this case, you are required to specify an algorithm.

Set PerformAutoML to true to have Amazon Forecast perform AutoML. This is a good option if you aren't sure which algorithm is suitable for your training data. In this case, PerformHPO must be false.

PerformHPO => Bool

Whether to perform hyperparameter optimization (HPO). HPO finds optimal hyperparameter values for your training data. The process of performing HPO is known as running a hyperparameter tuning job.

The default value is false. In this case, Amazon Forecast uses default hyperparameter values from the chosen algorithm.

To override the default values, set PerformHPO to true and, optionally, supply the HyperParameterTuningJobConfig object. The tuning job specifies a metric to optimize, which hyperparameters participate in tuning, and the valid range for each tunable hyperparameter. In this case, you are required to specify an algorithm and PerformAutoML must be false.

The following algorithm supports HPO:

• DeepAR+

REQUIRED PredictorName => Str

A name for the predictor.

TrainingParameters => Paws::Forecast::TrainingParameters

The hyperparameters to override for model training. The hyperparameters that you can override are listed in the individual algorithms. For the list of supported algorithms, see aws-forecast-choosing-recipes.

SEE ALSO

This class forms part of Paws, documenting arguments for method CreatePredictor in Paws::Forecast

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

cpanm Paws::SDK::Config

perl -MCPAN -e shell
install Paws::SDK::Config