AWS SDK for C++  1.9.47
AWS SDK for C++
Public Member Functions | List of all members
Aws::ForecastService::Model::CreatePredictorRequest Class Reference

#include <CreatePredictorRequest.h>

+ Inheritance diagram for Aws::ForecastService::Model::CreatePredictorRequest:

Public Member Functions

 CreatePredictorRequest ()
 
virtual const char * GetServiceRequestName () const override
 
Aws::String SerializePayload () const override
 
Aws::Http::HeaderValueCollection GetRequestSpecificHeaders () const override
 
const Aws::StringGetPredictorName () const
 
bool PredictorNameHasBeenSet () const
 
void SetPredictorName (const Aws::String &value)
 
void SetPredictorName (Aws::String &&value)
 
void SetPredictorName (const char *value)
 
CreatePredictorRequestWithPredictorName (const Aws::String &value)
 
CreatePredictorRequestWithPredictorName (Aws::String &&value)
 
CreatePredictorRequestWithPredictorName (const char *value)
 
const Aws::StringGetAlgorithmArn () const
 
bool AlgorithmArnHasBeenSet () const
 
void SetAlgorithmArn (const Aws::String &value)
 
void SetAlgorithmArn (Aws::String &&value)
 
void SetAlgorithmArn (const char *value)
 
CreatePredictorRequestWithAlgorithmArn (const Aws::String &value)
 
CreatePredictorRequestWithAlgorithmArn (Aws::String &&value)
 
CreatePredictorRequestWithAlgorithmArn (const char *value)
 
int GetForecastHorizon () const
 
bool ForecastHorizonHasBeenSet () const
 
void SetForecastHorizon (int value)
 
CreatePredictorRequestWithForecastHorizon (int value)
 
const Aws::Vector< Aws::String > & GetForecastTypes () const
 
bool ForecastTypesHasBeenSet () const
 
void SetForecastTypes (const Aws::Vector< Aws::String > &value)
 
void SetForecastTypes (Aws::Vector< Aws::String > &&value)
 
CreatePredictorRequestWithForecastTypes (const Aws::Vector< Aws::String > &value)
 
CreatePredictorRequestWithForecastTypes (Aws::Vector< Aws::String > &&value)
 
CreatePredictorRequestAddForecastTypes (const Aws::String &value)
 
CreatePredictorRequestAddForecastTypes (Aws::String &&value)
 
CreatePredictorRequestAddForecastTypes (const char *value)
 
bool GetPerformAutoML () const
 
bool PerformAutoMLHasBeenSet () const
 
void SetPerformAutoML (bool value)
 
CreatePredictorRequestWithPerformAutoML (bool value)
 
const AutoMLOverrideStrategyGetAutoMLOverrideStrategy () const
 
bool AutoMLOverrideStrategyHasBeenSet () const
 
void SetAutoMLOverrideStrategy (const AutoMLOverrideStrategy &value)
 
void SetAutoMLOverrideStrategy (AutoMLOverrideStrategy &&value)
 
CreatePredictorRequestWithAutoMLOverrideStrategy (const AutoMLOverrideStrategy &value)
 
CreatePredictorRequestWithAutoMLOverrideStrategy (AutoMLOverrideStrategy &&value)
 
bool GetPerformHPO () const
 
bool PerformHPOHasBeenSet () const
 
void SetPerformHPO (bool value)
 
CreatePredictorRequestWithPerformHPO (bool value)
 
const Aws::Map< Aws::String, Aws::String > & GetTrainingParameters () const
 
bool TrainingParametersHasBeenSet () const
 
void SetTrainingParameters (const Aws::Map< Aws::String, Aws::String > &value)
 
void SetTrainingParameters (Aws::Map< Aws::String, Aws::String > &&value)
 
CreatePredictorRequestWithTrainingParameters (const Aws::Map< Aws::String, Aws::String > &value)
 
CreatePredictorRequestWithTrainingParameters (Aws::Map< Aws::String, Aws::String > &&value)
 
CreatePredictorRequestAddTrainingParameters (const Aws::String &key, const Aws::String &value)
 
CreatePredictorRequestAddTrainingParameters (Aws::String &&key, const Aws::String &value)
 
CreatePredictorRequestAddTrainingParameters (const Aws::String &key, Aws::String &&value)
 
CreatePredictorRequestAddTrainingParameters (Aws::String &&key, Aws::String &&value)
 
CreatePredictorRequestAddTrainingParameters (const char *key, Aws::String &&value)
 
CreatePredictorRequestAddTrainingParameters (Aws::String &&key, const char *value)
 
CreatePredictorRequestAddTrainingParameters (const char *key, const char *value)
 
const EvaluationParametersGetEvaluationParameters () const
 
bool EvaluationParametersHasBeenSet () const
 
void SetEvaluationParameters (const EvaluationParameters &value)
 
void SetEvaluationParameters (EvaluationParameters &&value)
 
CreatePredictorRequestWithEvaluationParameters (const EvaluationParameters &value)
 
CreatePredictorRequestWithEvaluationParameters (EvaluationParameters &&value)
 
const HyperParameterTuningJobConfigGetHPOConfig () const
 
bool HPOConfigHasBeenSet () const
 
void SetHPOConfig (const HyperParameterTuningJobConfig &value)
 
void SetHPOConfig (HyperParameterTuningJobConfig &&value)
 
CreatePredictorRequestWithHPOConfig (const HyperParameterTuningJobConfig &value)
 
CreatePredictorRequestWithHPOConfig (HyperParameterTuningJobConfig &&value)
 
const InputDataConfigGetInputDataConfig () const
 
bool InputDataConfigHasBeenSet () const
 
void SetInputDataConfig (const InputDataConfig &value)
 
void SetInputDataConfig (InputDataConfig &&value)
 
CreatePredictorRequestWithInputDataConfig (const InputDataConfig &value)
 
CreatePredictorRequestWithInputDataConfig (InputDataConfig &&value)
 
const FeaturizationConfigGetFeaturizationConfig () const
 
bool FeaturizationConfigHasBeenSet () const
 
void SetFeaturizationConfig (const FeaturizationConfig &value)
 
void SetFeaturizationConfig (FeaturizationConfig &&value)
 
CreatePredictorRequestWithFeaturizationConfig (const FeaturizationConfig &value)
 
CreatePredictorRequestWithFeaturizationConfig (FeaturizationConfig &&value)
 
const EncryptionConfigGetEncryptionConfig () const
 
bool EncryptionConfigHasBeenSet () const
 
void SetEncryptionConfig (const EncryptionConfig &value)
 
void SetEncryptionConfig (EncryptionConfig &&value)
 
CreatePredictorRequestWithEncryptionConfig (const EncryptionConfig &value)
 
CreatePredictorRequestWithEncryptionConfig (EncryptionConfig &&value)
 
const Aws::Vector< Tag > & GetTags () const
 
bool TagsHasBeenSet () const
 
void SetTags (const Aws::Vector< Tag > &value)
 
void SetTags (Aws::Vector< Tag > &&value)
 
CreatePredictorRequestWithTags (const Aws::Vector< Tag > &value)
 
CreatePredictorRequestWithTags (Aws::Vector< Tag > &&value)
 
CreatePredictorRequestAddTags (const Tag &value)
 
CreatePredictorRequestAddTags (Tag &&value)
 
- Public Member Functions inherited from Aws::ForecastService::ForecastServiceRequest
virtual ~ForecastServiceRequest ()
 
void AddParametersToRequest (Aws::Http::HttpRequest &httpRequest) const
 
Aws::Http::HeaderValueCollection GetHeaders () const override
 
- Public Member Functions inherited from Aws::AmazonSerializableWebServiceRequest
 AmazonSerializableWebServiceRequest ()
 
virtual ~AmazonSerializableWebServiceRequest ()
 
std::shared_ptr< Aws::IOStreamGetBody () const override
 
- Public Member Functions inherited from Aws::AmazonWebServiceRequest
 AmazonWebServiceRequest ()
 
virtual ~AmazonWebServiceRequest ()=default
 
virtual void AddQueryStringParameters (Aws::Http::URI &uri) const
 
virtual void PutToPresignedUrl (Aws::Http::URI &uri) const
 
virtual bool IsStreaming () const
 
virtual bool IsEventStreamRequest () const
 
virtual bool SignBody () const
 
virtual bool IsChunked () const
 
virtual void SetRequestSignedHandler (const RequestSignedHandler &handler)
 
virtual const RequestSignedHandlerGetRequestSignedHandler () const
 
const Aws::IOStreamFactoryGetResponseStreamFactory () const
 
void SetResponseStreamFactory (const Aws::IOStreamFactory &factory)
 
virtual void SetDataReceivedEventHandler (const Aws::Http::DataReceivedEventHandler &dataReceivedEventHandler)
 
virtual void SetDataSentEventHandler (const Aws::Http::DataSentEventHandler &dataSentEventHandler)
 
virtual void SetContinueRequestHandler (const Aws::Http::ContinueRequestHandler &continueRequestHandler)
 
virtual void SetDataReceivedEventHandler (Aws::Http::DataReceivedEventHandler &&dataReceivedEventHandler)
 
virtual void SetDataSentEventHandler (Aws::Http::DataSentEventHandler &&dataSentEventHandler)
 
virtual void SetContinueRequestHandler (Aws::Http::ContinueRequestHandler &&continueRequestHandler)
 
virtual void SetRequestRetryHandler (const RequestRetryHandler &handler)
 
virtual void SetRequestRetryHandler (RequestRetryHandler &&handler)
 
virtual const Aws::Http::DataReceivedEventHandlerGetDataReceivedEventHandler () const
 
virtual const Aws::Http::DataSentEventHandlerGetDataSentEventHandler () const
 
virtual const Aws::Http::ContinueRequestHandlerGetContinueRequestHandler () const
 
virtual const RequestRetryHandlerGetRequestRetryHandler () const
 
virtual bool ShouldComputeContentMd5 () const
 

Additional Inherited Members

- Protected Member Functions inherited from Aws::AmazonWebServiceRequest
virtual void DumpBodyToUrl (Aws::Http::URI &uri) const
 

Detailed Description

Definition at line 30 of file CreatePredictorRequest.h.

Constructor & Destructor Documentation

◆ CreatePredictorRequest()

Aws::ForecastService::Model::CreatePredictorRequest::CreatePredictorRequest ( )

Member Function Documentation

◆ AddForecastTypes() [1/3]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddForecastTypes ( Aws::String &&  value)
inline

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"].

Definition at line 307 of file CreatePredictorRequest.h.

◆ AddForecastTypes() [2/3]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddForecastTypes ( const Aws::String value)
inline

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"].

Definition at line 298 of file CreatePredictorRequest.h.

◆ AddForecastTypes() [3/3]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddForecastTypes ( const char *  value)
inline

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"].

Definition at line 316 of file CreatePredictorRequest.h.

◆ AddTags() [1/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddTags ( const Tag value)
inline

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.

Definition at line 944 of file CreatePredictorRequest.h.

◆ AddTags() [2/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddTags ( Tag &&  value)
inline

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.

Definition at line 967 of file CreatePredictorRequest.h.

◆ AddTrainingParameters() [1/7]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddTrainingParameters ( Aws::String &&  key,
Aws::String &&  value 
)
inline

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.

Definition at line 546 of file CreatePredictorRequest.h.

◆ AddTrainingParameters() [2/7]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddTrainingParameters ( Aws::String &&  key,
const Aws::String value 
)
inline

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.

Definition at line 532 of file CreatePredictorRequest.h.

◆ AddTrainingParameters() [3/7]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddTrainingParameters ( Aws::String &&  key,
const char *  value 
)
inline

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.

Definition at line 560 of file CreatePredictorRequest.h.

◆ AddTrainingParameters() [4/7]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddTrainingParameters ( const Aws::String key,
Aws::String &&  value 
)
inline

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.

Definition at line 539 of file CreatePredictorRequest.h.

◆ AddTrainingParameters() [5/7]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddTrainingParameters ( const Aws::String key,
const Aws::String value 
)
inline

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.

Definition at line 525 of file CreatePredictorRequest.h.

◆ AddTrainingParameters() [6/7]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddTrainingParameters ( const char *  key,
Aws::String &&  value 
)
inline

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.

Definition at line 553 of file CreatePredictorRequest.h.

◆ AddTrainingParameters() [7/7]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::AddTrainingParameters ( const char *  key,
const char *  value 
)
inline

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.

Definition at line 567 of file CreatePredictorRequest.h.

◆ AlgorithmArnHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::AlgorithmArnHasBeenSet ( ) const
inline

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

Definition at line 111 of file CreatePredictorRequest.h.

◆ AutoMLOverrideStrategyHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::AutoMLOverrideStrategyHasBeenSet ( ) const
inline

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.

Definition at line 378 of file CreatePredictorRequest.h.

◆ EncryptionConfigHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::EncryptionConfigHasBeenSet ( ) const
inline

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.

Definition at line 758 of file CreatePredictorRequest.h.

◆ EvaluationParametersHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::EvaluationParametersHasBeenSet ( ) const
inline

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.

Definition at line 584 of file CreatePredictorRequest.h.

◆ FeaturizationConfigHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::FeaturizationConfigHasBeenSet ( ) const
inline

The featurization configuration.

Definition at line 725 of file CreatePredictorRequest.h.

◆ ForecastHorizonHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::ForecastHorizonHasBeenSet ( ) const
inline

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.

Definition at line 212 of file CreatePredictorRequest.h.

◆ ForecastTypesHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::ForecastTypesHasBeenSet ( ) const
inline

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"].

Definition at line 253 of file CreatePredictorRequest.h.

◆ GetAlgorithmArn()

const Aws::String& Aws::ForecastService::Model::CreatePredictorRequest::GetAlgorithmArn ( ) const
inline

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

Definition at line 98 of file CreatePredictorRequest.h.

◆ GetAutoMLOverrideStrategy()

const AutoMLOverrideStrategy& Aws::ForecastService::Model::CreatePredictorRequest::GetAutoMLOverrideStrategy ( ) const
inline

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.

Definition at line 370 of file CreatePredictorRequest.h.

◆ GetEncryptionConfig()

const EncryptionConfig& Aws::ForecastService::Model::CreatePredictorRequest::GetEncryptionConfig ( ) const
inline

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.

Definition at line 752 of file CreatePredictorRequest.h.

◆ GetEvaluationParameters()

const EvaluationParameters& Aws::ForecastService::Model::CreatePredictorRequest::GetEvaluationParameters ( ) const
inline

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.

Definition at line 576 of file CreatePredictorRequest.h.

◆ GetFeaturizationConfig()

const FeaturizationConfig& Aws::ForecastService::Model::CreatePredictorRequest::GetFeaturizationConfig ( ) const
inline

The featurization configuration.

Definition at line 720 of file CreatePredictorRequest.h.

◆ GetForecastHorizon()

int Aws::ForecastService::Model::CreatePredictorRequest::GetForecastHorizon ( ) const
inline

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.

Definition at line 201 of file CreatePredictorRequest.h.

◆ GetForecastTypes()

const Aws::Vector<Aws::String>& Aws::ForecastService::Model::CreatePredictorRequest::GetForecastTypes ( ) const
inline

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"].

Definition at line 244 of file CreatePredictorRequest.h.

◆ GetHPOConfig()

const HyperParameterTuningJobConfig& Aws::ForecastService::Model::CreatePredictorRequest::GetHPOConfig ( ) const
inline

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.

Definition at line 627 of file CreatePredictorRequest.h.

◆ GetInputDataConfig()

const InputDataConfig& Aws::ForecastService::Model::CreatePredictorRequest::GetInputDataConfig ( ) const
inline

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

Definition at line 684 of file CreatePredictorRequest.h.

◆ GetPerformAutoML()

bool Aws::ForecastService::Model::CreatePredictorRequest::GetPerformAutoML ( ) const
inline

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.

Definition at line 328 of file CreatePredictorRequest.h.

◆ GetPerformHPO()

bool Aws::ForecastService::Model::CreatePredictorRequest::GetPerformHPO ( ) const
inline

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

Definition at line 427 of file CreatePredictorRequest.h.

◆ GetPredictorName()

const Aws::String& Aws::ForecastService::Model::CreatePredictorRequest::GetPredictorName ( ) const
inline

A name for the predictor.

Definition at line 49 of file CreatePredictorRequest.h.

◆ GetRequestSpecificHeaders()

Aws::Http::HeaderValueCollection Aws::ForecastService::Model::CreatePredictorRequest::GetRequestSpecificHeaders ( ) const
overridevirtual

◆ GetServiceRequestName()

virtual const char* Aws::ForecastService::Model::CreatePredictorRequest::GetServiceRequestName ( ) const
inlineoverridevirtual

Implements Aws::AmazonWebServiceRequest.

Definition at line 39 of file CreatePredictorRequest.h.

◆ GetTags()

const Aws::Vector<Tag>& Aws::ForecastService::Model::CreatePredictorRequest::GetTags ( ) const
inline

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.

Definition at line 806 of file CreatePredictorRequest.h.

◆ GetTrainingParameters()

const Aws::Map<Aws::String, Aws::String>& Aws::ForecastService::Model::CreatePredictorRequest::GetTrainingParameters ( ) const
inline

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.

Definition at line 483 of file CreatePredictorRequest.h.

◆ HPOConfigHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::HPOConfigHasBeenSet ( ) const
inline

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.

Definition at line 637 of file CreatePredictorRequest.h.

◆ InputDataConfigHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::InputDataConfigHasBeenSet ( ) const
inline

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

Definition at line 690 of file CreatePredictorRequest.h.

◆ PerformAutoMLHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::PerformAutoMLHasBeenSet ( ) const
inline

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.

Definition at line 339 of file CreatePredictorRequest.h.

◆ PerformHPOHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::PerformHPOHasBeenSet ( ) const
inline

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

Definition at line 443 of file CreatePredictorRequest.h.

◆ PredictorNameHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::PredictorNameHasBeenSet ( ) const
inline

A name for the predictor.

Definition at line 54 of file CreatePredictorRequest.h.

◆ SerializePayload()

Aws::String Aws::ForecastService::Model::CreatePredictorRequest::SerializePayload ( ) const
overridevirtual

Convert payload into String.

Implements Aws::AmazonSerializableWebServiceRequest.

◆ SetAlgorithmArn() [1/3]

void Aws::ForecastService::Model::CreatePredictorRequest::SetAlgorithmArn ( Aws::String &&  value)
inline

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

Definition at line 137 of file CreatePredictorRequest.h.

◆ SetAlgorithmArn() [2/3]

void Aws::ForecastService::Model::CreatePredictorRequest::SetAlgorithmArn ( const Aws::String value)
inline

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

Definition at line 124 of file CreatePredictorRequest.h.

◆ SetAlgorithmArn() [3/3]

void Aws::ForecastService::Model::CreatePredictorRequest::SetAlgorithmArn ( const char *  value)
inline

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

Definition at line 150 of file CreatePredictorRequest.h.

◆ SetAutoMLOverrideStrategy() [1/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetAutoMLOverrideStrategy ( AutoMLOverrideStrategy &&  value)
inline

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.

Definition at line 394 of file CreatePredictorRequest.h.

◆ SetAutoMLOverrideStrategy() [2/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetAutoMLOverrideStrategy ( const AutoMLOverrideStrategy value)
inline

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.

Definition at line 386 of file CreatePredictorRequest.h.

◆ SetEncryptionConfig() [1/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetEncryptionConfig ( const EncryptionConfig value)
inline

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.

Definition at line 764 of file CreatePredictorRequest.h.

◆ SetEncryptionConfig() [2/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetEncryptionConfig ( EncryptionConfig &&  value)
inline

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.

Definition at line 770 of file CreatePredictorRequest.h.

◆ SetEvaluationParameters() [1/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetEvaluationParameters ( const EvaluationParameters value)
inline

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.

Definition at line 592 of file CreatePredictorRequest.h.

◆ SetEvaluationParameters() [2/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetEvaluationParameters ( EvaluationParameters &&  value)
inline

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.

Definition at line 600 of file CreatePredictorRequest.h.

◆ SetFeaturizationConfig() [1/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetFeaturizationConfig ( const FeaturizationConfig value)
inline

The featurization configuration.

Definition at line 730 of file CreatePredictorRequest.h.

◆ SetFeaturizationConfig() [2/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetFeaturizationConfig ( FeaturizationConfig &&  value)
inline

The featurization configuration.

Definition at line 735 of file CreatePredictorRequest.h.

◆ SetForecastHorizon()

void Aws::ForecastService::Model::CreatePredictorRequest::SetForecastHorizon ( int  value)
inline

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.

Definition at line 223 of file CreatePredictorRequest.h.

◆ SetForecastTypes() [1/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetForecastTypes ( Aws::Vector< Aws::String > &&  value)
inline

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"].

Definition at line 271 of file CreatePredictorRequest.h.

◆ SetForecastTypes() [2/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetForecastTypes ( const Aws::Vector< Aws::String > &  value)
inline

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"].

Definition at line 262 of file CreatePredictorRequest.h.

◆ SetHPOConfig() [1/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetHPOConfig ( const HyperParameterTuningJobConfig value)
inline

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.

Definition at line 647 of file CreatePredictorRequest.h.

◆ SetHPOConfig() [2/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetHPOConfig ( HyperParameterTuningJobConfig &&  value)
inline

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.

Definition at line 657 of file CreatePredictorRequest.h.

◆ SetInputDataConfig() [1/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetInputDataConfig ( const InputDataConfig value)
inline

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

Definition at line 696 of file CreatePredictorRequest.h.

◆ SetInputDataConfig() [2/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetInputDataConfig ( InputDataConfig &&  value)
inline

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

Definition at line 702 of file CreatePredictorRequest.h.

◆ SetPerformAutoML()

void Aws::ForecastService::Model::CreatePredictorRequest::SetPerformAutoML ( bool  value)
inline

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.

Definition at line 350 of file CreatePredictorRequest.h.

◆ SetPerformHPO()

void Aws::ForecastService::Model::CreatePredictorRequest::SetPerformHPO ( bool  value)
inline

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

Definition at line 459 of file CreatePredictorRequest.h.

◆ SetPredictorName() [1/3]

void Aws::ForecastService::Model::CreatePredictorRequest::SetPredictorName ( Aws::String &&  value)
inline

A name for the predictor.

Definition at line 64 of file CreatePredictorRequest.h.

◆ SetPredictorName() [2/3]

void Aws::ForecastService::Model::CreatePredictorRequest::SetPredictorName ( const Aws::String value)
inline

A name for the predictor.

Definition at line 59 of file CreatePredictorRequest.h.

◆ SetPredictorName() [3/3]

void Aws::ForecastService::Model::CreatePredictorRequest::SetPredictorName ( const char *  value)
inline

A name for the predictor.

Definition at line 69 of file CreatePredictorRequest.h.

◆ SetTags() [1/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetTags ( Aws::Vector< Tag > &&  value)
inline

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.

Definition at line 875 of file CreatePredictorRequest.h.

◆ SetTags() [2/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetTags ( const Aws::Vector< Tag > &  value)
inline

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.

Definition at line 852 of file CreatePredictorRequest.h.

◆ SetTrainingParameters() [1/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetTrainingParameters ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

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.

Definition at line 504 of file CreatePredictorRequest.h.

◆ SetTrainingParameters() [2/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetTrainingParameters ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

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.

Definition at line 497 of file CreatePredictorRequest.h.

◆ TagsHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::TagsHasBeenSet ( ) const
inline

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.

Definition at line 829 of file CreatePredictorRequest.h.

◆ TrainingParametersHasBeenSet()

bool Aws::ForecastService::Model::CreatePredictorRequest::TrainingParametersHasBeenSet ( ) const
inline

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.

Definition at line 490 of file CreatePredictorRequest.h.

◆ WithAlgorithmArn() [1/3]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithAlgorithmArn ( Aws::String &&  value)
inline

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

Definition at line 176 of file CreatePredictorRequest.h.

◆ WithAlgorithmArn() [2/3]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithAlgorithmArn ( const Aws::String value)
inline

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

Definition at line 163 of file CreatePredictorRequest.h.

◆ WithAlgorithmArn() [3/3]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithAlgorithmArn ( const char *  value)
inline

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

Definition at line 189 of file CreatePredictorRequest.h.

◆ WithAutoMLOverrideStrategy() [1/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithAutoMLOverrideStrategy ( AutoMLOverrideStrategy &&  value)
inline

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.

Definition at line 410 of file CreatePredictorRequest.h.

◆ WithAutoMLOverrideStrategy() [2/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithAutoMLOverrideStrategy ( const AutoMLOverrideStrategy value)
inline

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.

Definition at line 402 of file CreatePredictorRequest.h.

◆ WithEncryptionConfig() [1/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithEncryptionConfig ( const EncryptionConfig value)
inline

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.

Definition at line 776 of file CreatePredictorRequest.h.

◆ WithEncryptionConfig() [2/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithEncryptionConfig ( EncryptionConfig &&  value)
inline

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.

Definition at line 782 of file CreatePredictorRequest.h.

◆ WithEvaluationParameters() [1/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithEvaluationParameters ( const EvaluationParameters value)
inline

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.

Definition at line 608 of file CreatePredictorRequest.h.

◆ WithEvaluationParameters() [2/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithEvaluationParameters ( EvaluationParameters &&  value)
inline

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.

Definition at line 616 of file CreatePredictorRequest.h.

◆ WithFeaturizationConfig() [1/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithFeaturizationConfig ( const FeaturizationConfig value)
inline

The featurization configuration.

Definition at line 740 of file CreatePredictorRequest.h.

◆ WithFeaturizationConfig() [2/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithFeaturizationConfig ( FeaturizationConfig &&  value)
inline

The featurization configuration.

Definition at line 745 of file CreatePredictorRequest.h.

◆ WithForecastHorizon()

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithForecastHorizon ( int  value)
inline

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.

Definition at line 234 of file CreatePredictorRequest.h.

◆ WithForecastTypes() [1/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithForecastTypes ( Aws::Vector< Aws::String > &&  value)
inline

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"].

Definition at line 289 of file CreatePredictorRequest.h.

◆ WithForecastTypes() [2/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithForecastTypes ( const Aws::Vector< Aws::String > &  value)
inline

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"].

Definition at line 280 of file CreatePredictorRequest.h.

◆ WithHPOConfig() [1/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithHPOConfig ( const HyperParameterTuningJobConfig value)
inline

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.

Definition at line 667 of file CreatePredictorRequest.h.

◆ WithHPOConfig() [2/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithHPOConfig ( HyperParameterTuningJobConfig &&  value)
inline

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.

Definition at line 677 of file CreatePredictorRequest.h.

◆ WithInputDataConfig() [1/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithInputDataConfig ( const InputDataConfig value)
inline

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

Definition at line 708 of file CreatePredictorRequest.h.

◆ WithInputDataConfig() [2/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithInputDataConfig ( InputDataConfig &&  value)
inline

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

Definition at line 714 of file CreatePredictorRequest.h.

◆ WithPerformAutoML()

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithPerformAutoML ( bool  value)
inline

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.

Definition at line 361 of file CreatePredictorRequest.h.

◆ WithPerformHPO()

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithPerformHPO ( bool  value)
inline

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

Definition at line 475 of file CreatePredictorRequest.h.

◆ WithPredictorName() [1/3]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithPredictorName ( Aws::String &&  value)
inline

A name for the predictor.

Definition at line 79 of file CreatePredictorRequest.h.

◆ WithPredictorName() [2/3]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithPredictorName ( const Aws::String value)
inline

A name for the predictor.

Definition at line 74 of file CreatePredictorRequest.h.

◆ WithPredictorName() [3/3]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithPredictorName ( const char *  value)
inline

A name for the predictor.

Definition at line 84 of file CreatePredictorRequest.h.

◆ WithTags() [1/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithTags ( Aws::Vector< Tag > &&  value)
inline

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.

Definition at line 921 of file CreatePredictorRequest.h.

◆ WithTags() [2/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithTags ( const Aws::Vector< Tag > &  value)
inline

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.

Definition at line 898 of file CreatePredictorRequest.h.

◆ WithTrainingParameters() [1/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithTrainingParameters ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

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.

Definition at line 518 of file CreatePredictorRequest.h.

◆ WithTrainingParameters() [2/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithTrainingParameters ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

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.

Definition at line 511 of file CreatePredictorRequest.h.


The documentation for this class was generated from the following file: