AWS SDK for C++  1.9.161
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)
 
const OptimizationMetricGetOptimizationMetric () const
 
bool OptimizationMetricHasBeenSet () const
 
void SetOptimizationMetric (const OptimizationMetric &value)
 
void SetOptimizationMetric (OptimizationMetric &&value)
 
CreatePredictorRequestWithOptimizationMetric (const OptimizationMetric &value)
 
CreatePredictorRequestWithOptimizationMetric (OptimizationMetric &&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 31 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 308 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 299 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 317 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 957 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 980 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 559 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 545 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 573 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 552 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 538 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 566 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 580 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 112 of file CreatePredictorRequest.h.

◆ AutoMLOverrideStrategyHasBeenSet()

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

The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.

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 383 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 771 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 597 of file CreatePredictorRequest.h.

◆ FeaturizationConfigHasBeenSet()

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

The featurization configuration.

Definition at line 738 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 213 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 254 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 99 of file CreatePredictorRequest.h.

◆ GetAutoMLOverrideStrategy()

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

The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.

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 373 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 765 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 589 of file CreatePredictorRequest.h.

◆ GetFeaturizationConfig()

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

The featurization configuration.

Definition at line 733 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 202 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 245 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 640 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 697 of file CreatePredictorRequest.h.

◆ GetOptimizationMetric()

const OptimizationMetric& Aws::ForecastService::Model::CreatePredictorRequest::GetOptimizationMetric ( ) const
inline

The accuracy metric used to optimize the predictor.

Definition at line 986 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 329 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 440 of file CreatePredictorRequest.h.

◆ GetPredictorName()

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

A name for the predictor.

Definition at line 50 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 40 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 819 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 496 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 650 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 703 of file CreatePredictorRequest.h.

◆ OptimizationMetricHasBeenSet()

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

The accuracy metric used to optimize the predictor.

Definition at line 991 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 340 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 456 of file CreatePredictorRequest.h.

◆ PredictorNameHasBeenSet()

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

A name for the predictor.

Definition at line 55 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 138 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 125 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 151 of file CreatePredictorRequest.h.

◆ SetAutoMLOverrideStrategy() [1/2]

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

The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.

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 403 of file CreatePredictorRequest.h.

◆ SetAutoMLOverrideStrategy() [2/2]

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

The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.

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 393 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 777 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 783 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 605 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 613 of file CreatePredictorRequest.h.

◆ SetFeaturizationConfig() [1/2]

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

The featurization configuration.

Definition at line 743 of file CreatePredictorRequest.h.

◆ SetFeaturizationConfig() [2/2]

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

The featurization configuration.

Definition at line 748 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 224 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 272 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 263 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 660 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 670 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 709 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 715 of file CreatePredictorRequest.h.

◆ SetOptimizationMetric() [1/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetOptimizationMetric ( const OptimizationMetric value)
inline

The accuracy metric used to optimize the predictor.

Definition at line 996 of file CreatePredictorRequest.h.

◆ SetOptimizationMetric() [2/2]

void Aws::ForecastService::Model::CreatePredictorRequest::SetOptimizationMetric ( OptimizationMetric &&  value)
inline

The accuracy metric used to optimize the predictor.

Definition at line 1001 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 351 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 472 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 65 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 60 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 70 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 888 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 865 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 517 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 510 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 842 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 503 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 177 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 164 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 190 of file CreatePredictorRequest.h.

◆ WithAutoMLOverrideStrategy() [1/2]

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

The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.

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 423 of file CreatePredictorRequest.h.

◆ WithAutoMLOverrideStrategy() [2/2]

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

The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.

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 413 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 789 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 795 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 621 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 629 of file CreatePredictorRequest.h.

◆ WithFeaturizationConfig() [1/2]

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

The featurization configuration.

Definition at line 753 of file CreatePredictorRequest.h.

◆ WithFeaturizationConfig() [2/2]

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

The featurization configuration.

Definition at line 758 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 235 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 290 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 281 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 680 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 690 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 721 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 727 of file CreatePredictorRequest.h.

◆ WithOptimizationMetric() [1/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithOptimizationMetric ( const OptimizationMetric value)
inline

The accuracy metric used to optimize the predictor.

Definition at line 1006 of file CreatePredictorRequest.h.

◆ WithOptimizationMetric() [2/2]

CreatePredictorRequest& Aws::ForecastService::Model::CreatePredictorRequest::WithOptimizationMetric ( OptimizationMetric &&  value)
inline

The accuracy metric used to optimize the predictor.

Definition at line 1011 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 362 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 488 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 80 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 75 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 85 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 934 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 911 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 531 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 524 of file CreatePredictorRequest.h.


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