class Aws::ForecastService::Types::CreatePredictorRequest

@note When making an API call, you may pass CreatePredictorRequest

data as a hash:

    {
      predictor_name: "Name", # required
      algorithm_arn: "Arn",
      forecast_horizon: 1, # required
      forecast_types: ["ForecastType"],
      perform_auto_ml: false,
      auto_ml_override_strategy: "LatencyOptimized", # accepts LatencyOptimized
      perform_hpo: false,
      training_parameters: {
        "ParameterKey" => "ParameterValue",
      },
      evaluation_parameters: {
        number_of_backtest_windows: 1,
        back_test_window_offset: 1,
      },
      hpo_config: {
        parameter_ranges: {
          categorical_parameter_ranges: [
            {
              name: "Name", # required
              values: ["Value"], # required
            },
          ],
          continuous_parameter_ranges: [
            {
              name: "Name", # required
              max_value: 1.0, # required
              min_value: 1.0, # required
              scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
            },
          ],
          integer_parameter_ranges: [
            {
              name: "Name", # required
              max_value: 1, # required
              min_value: 1, # required
              scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
            },
          ],
        },
      },
      input_data_config: { # required
        dataset_group_arn: "Arn", # required
        supplementary_features: [
          {
            name: "Name", # required
            value: "Value", # required
          },
        ],
      },
      featurization_config: { # required
        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",
                },
              },
            ],
          },
        ],
      },
      encryption_config: {
        role_arn: "Arn", # required
        kms_key_arn: "KMSKeyArn", # required
      },
      tags: [
        {
          key: "TagKey", # required
          value: "TagValue", # required
        },
      ],
      optimization_metric: "WAPE", # accepts WAPE, RMSE, AverageWeightedQuantileLoss, MASE, MAPE
    }

@!attribute [rw] predictor_name

A name for the predictor.
@return [String]

@!attribute [rw] algorithm_arn

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/CNN-QR`

* `arn:aws:forecast:::algorithm/Deep_AR_Plus`

* `arn:aws:forecast:::algorithm/ETS`

* `arn:aws:forecast:::algorithm/NPTS`

* `arn:aws:forecast:::algorithm/Prophet`
@return [String]

@!attribute [rw] forecast_horizon

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.
@return [Integer]

@!attribute [rw] forecast_types

Specifies the forecast types used to train a predictor. You can
specify up to five forecast types. Forecast types can be quantiles
from 0.01 to 0.99, by increments of 0.01 or higher. You can also
specify the mean forecast with `mean`.

The default value is `["0.10", "0.50", "0.9"]`.
@return [Array<String>]

@!attribute [rw] perform_auto_ml

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.
@return [Boolean]

@!attribute [rw] auto_ml_override_strategy

<note markdown="1"> The `LatencyOptimized` AutoML override strategy is only available in
private beta. Contact AWS Support or your account manager to learn
more about access privileges.

 </note>

Used to overide the default AutoML strategy, which is to optimize
predictor accuracy. To apply an AutoML strategy that minimizes
training time, use `LatencyOptimized`.

This parameter is only valid for predictors trained using AutoML.
@return [String]

@!attribute [rw] perform_hpo

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 algorithms support HPO:

* DeepAR+

* CNN-QR
@return [Boolean]

@!attribute [rw] training_parameters

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.
@return [Hash<String,String>]

@!attribute [rw] evaluation_parameters

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.
@return [Types::EvaluationParameters]

@!attribute [rw] hpo_config

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.
@return [Types::HyperParameterTuningJobConfig]

@!attribute [rw] input_data_config

Describes the dataset group that contains the data to use to train
the predictor.
@return [Types::InputDataConfig]

@!attribute [rw] featurization_config

The featurization configuration.
@return [Types::FeaturizationConfig]

@!attribute [rw] encryption_config

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.
@return [Types::EncryptionConfig]

@!attribute [rw] tags

The optional metadata that you apply to the predictor to help you
categorize and organize them. Each tag consists of a key and an
optional value, both of which you define.

The following basic restrictions apply to tags:

* Maximum number of tags per resource - 50.

* For each resource, each tag key must be unique, and each tag key
  can have only one value.

* Maximum key length - 128 Unicode characters in UTF-8.

* Maximum value length - 256 Unicode characters in UTF-8.

* If your tagging schema is used across multiple services and
  resources, remember that other services may have restrictions on
  allowed characters. Generally allowed characters are: letters,
  numbers, and spaces representable in UTF-8, and the following
  characters: + - = . \_ : / @.

* Tag keys and values are case sensitive.

* Do not use `aws:`, `AWS:`, or any upper or lowercase combination
  of such as a prefix for keys as it is reserved for AWS use. You
  cannot edit or delete tag keys with this prefix. Values can have
  this prefix. If a tag value has `aws` as its prefix but the key
  does not, then Forecast considers it to be a user tag and will
  count against the limit of 50 tags. Tags with only the key prefix
  of `aws` do not count against your tags per resource limit.
@return [Array<Types::Tag>]

@!attribute [rw] optimization_metric

The accuracy metric used to optimize the predictor.
@return [String]

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

Constants

SENSITIVE