Package software.amazon.awssdk.services.forecast.model

Class CreatePredictorRequest

    • Method Detail

      • predictorName

        public final String predictorName()

        A name for the predictor.

        Returns:
        A name for the predictor.

      • algorithmArn

        public final String algorithmArn()

        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

        Returns:
        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

      • forecastHorizon

        public final Integer forecastHorizon()

        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.

        Returns:
        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.

      • hasForecastTypes

        public final boolean hasForecastTypes()
        For responses, this returns true if the service returned a value for the ForecastTypes property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • forecastTypes

        public final List<String> forecastTypes()

        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"].

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasForecastTypes() method.

        Returns:
        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"].

      • performAutoML

        public final Boolean performAutoML()

        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.

        Returns:
        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.

      • autoMLOverrideStrategy

        public final AutoMLOverrideStrategy autoMLOverrideStrategy()

        The LatencyOptimized AutoML override strategy is only available in private beta. Contact Amazon Web Services 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.

        If the service returns an enum value that is not available in the current SDK version, autoMLOverrideStrategy will return AutoMLOverrideStrategy.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from autoMLOverrideStrategyAsString().

        Returns:

        The LatencyOptimized AutoML override strategy is only available in private beta. Contact Amazon Web Services 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.

        See Also:
        AutoMLOverrideStrategy

      • autoMLOverrideStrategyAsString

        public final String autoMLOverrideStrategyAsString()

        The LatencyOptimized AutoML override strategy is only available in private beta. Contact Amazon Web Services 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.

        If the service returns an enum value that is not available in the current SDK version, autoMLOverrideStrategy will return AutoMLOverrideStrategy.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from autoMLOverrideStrategyAsString().

        Returns:

        The LatencyOptimized AutoML override strategy is only available in private beta. Contact Amazon Web Services 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.

        See Also:
        AutoMLOverrideStrategy

      • performHPO

        public final Boolean performHPO()

        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

        Returns:
        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

      • hasTrainingParameters

        public final boolean hasTrainingParameters()
        For responses, this returns true if the service returned a value for the TrainingParameters property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • trainingParameters

        public final Map<String,​String> trainingParameters()

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

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasTrainingParameters() method.

        Returns:
        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.

      • evaluationParameters

        public final EvaluationParameters evaluationParameters()

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

        Returns:
        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.

      • hpoConfig

        public final HyperParameterTuningJobConfig hpoConfig()

        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.

        Returns:
        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.

      • inputDataConfig

        public final InputDataConfig inputDataConfig()

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

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

      • featurizationConfig

        public final FeaturizationConfig featurizationConfig()

        The featurization configuration.

        Returns:
        The featurization configuration.

      • encryptionConfig

        public final EncryptionConfig encryptionConfig()

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

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

      • hasTags

        public final boolean hasTags()
        For responses, this returns true if the service returned a value for the Tags property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • tags

        public final List<Tag> tags()

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

        The following basic restrictions apply to tags:

        • Maximum number of tags per resource - 50.

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

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

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

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

        • Tag keys and values are case sensitive.

        • Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services 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.

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasTags() method.

        Returns:
        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 Amazon Web Services 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.

      • optimizationMetricAsString

        public final String optimizationMetricAsString()

        The accuracy metric used to optimize the predictor.

        If the service returns an enum value that is not available in the current SDK version, optimizationMetric will return OptimizationMetric.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from optimizationMetricAsString().

        Returns:
        The accuracy metric used to optimize the predictor.
        See Also:
        OptimizationMetric

      • toString

        public final String toString()
        Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
        Overrides:
        toString in class Object