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