public static interface CreateAutoPredictorRequest.Builder extends ForecastRequest.Builder, SdkPojo, CopyableBuilder<CreateAutoPredictorRequest.Builder,CreateAutoPredictorRequest>
| Modifier and Type | Method and Description |
|---|---|
default CreateAutoPredictorRequest.Builder |
dataConfig(Consumer<DataConfig.Builder> dataConfig)
The data configuration for your dataset group and any additional datasets.
|
CreateAutoPredictorRequest.Builder |
dataConfig(DataConfig dataConfig)
The data configuration for your dataset group and any additional datasets.
|
default CreateAutoPredictorRequest.Builder |
encryptionConfig(Consumer<EncryptionConfig.Builder> encryptionConfig)
Sets the value of the EncryptionConfig property for this object.
|
CreateAutoPredictorRequest.Builder |
encryptionConfig(EncryptionConfig encryptionConfig)
Sets the value of the EncryptionConfig property for this object.
|
CreateAutoPredictorRequest.Builder |
explainPredictor(Boolean explainPredictor)
Create an Explainability resource for the predictor.
|
CreateAutoPredictorRequest.Builder |
forecastDimensions(Collection<String> forecastDimensions)
An array of dimension (field) names that specify how to group the generated forecast.
|
CreateAutoPredictorRequest.Builder |
forecastDimensions(String... forecastDimensions)
An array of dimension (field) names that specify how to group the generated forecast.
|
CreateAutoPredictorRequest.Builder |
forecastFrequency(String forecastFrequency)
The frequency of predictions in a forecast.
|
CreateAutoPredictorRequest.Builder |
forecastHorizon(Integer forecastHorizon)
The number of time-steps that the model predicts.
|
CreateAutoPredictorRequest.Builder |
forecastTypes(Collection<String> forecastTypes)
The forecast types used to train a predictor.
|
CreateAutoPredictorRequest.Builder |
forecastTypes(String... forecastTypes)
The forecast types used to train a predictor.
|
default CreateAutoPredictorRequest.Builder |
monitorConfig(Consumer<MonitorConfig.Builder> monitorConfig)
The configuration details for predictor monitoring.
|
CreateAutoPredictorRequest.Builder |
monitorConfig(MonitorConfig monitorConfig)
The configuration details for predictor monitoring.
|
CreateAutoPredictorRequest.Builder |
optimizationMetric(OptimizationMetric optimizationMetric)
The accuracy metric used to optimize the predictor.
|
CreateAutoPredictorRequest.Builder |
optimizationMetric(String optimizationMetric)
The accuracy metric used to optimize the predictor.
|
CreateAutoPredictorRequest.Builder |
overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration) |
CreateAutoPredictorRequest.Builder |
overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer) |
CreateAutoPredictorRequest.Builder |
predictorName(String predictorName)
A unique name for the predictor
|
CreateAutoPredictorRequest.Builder |
referencePredictorArn(String referencePredictorArn)
The ARN of the predictor to retrain or upgrade.
|
CreateAutoPredictorRequest.Builder |
tags(Collection<Tag> tags)
Optional metadata to help you categorize and organize your predictors.
|
CreateAutoPredictorRequest.Builder |
tags(Consumer<Tag.Builder>... tags)
Optional metadata to help you categorize and organize your predictors.
|
CreateAutoPredictorRequest.Builder |
tags(Tag... tags)
Optional metadata to help you categorize and organize your predictors.
|
default CreateAutoPredictorRequest.Builder |
timeAlignmentBoundary(Consumer<TimeAlignmentBoundary.Builder> timeAlignmentBoundary)
The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast
frequency.
|
CreateAutoPredictorRequest.Builder |
timeAlignmentBoundary(TimeAlignmentBoundary timeAlignmentBoundary)
The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast
frequency.
|
buildoverrideConfigurationequalsBySdkFields, sdkFieldscopyapplyMutation, buildCreateAutoPredictorRequest.Builder predictorName(String predictorName)
A unique name for the predictor
predictorName - A unique name for the predictorCreateAutoPredictorRequest.Builder forecastHorizon(Integer forecastHorizon)
The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.
The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the TARGET_TIME_SERIES dataset length. If you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.
If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the forecast horizon parameter. You can meet this requirement by providing longer time-series in the dataset.
forecastHorizon - The number of time-steps that the model predicts. The forecast horizon is also called the prediction
length.
The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the TARGET_TIME_SERIES dataset length. If you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.
If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the forecast horizon parameter. You can meet this requirement by providing longer time-series in the dataset.
CreateAutoPredictorRequest.Builder forecastTypes(Collection<String> forecastTypes)
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.
forecastTypes - 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.CreateAutoPredictorRequest.Builder forecastTypes(String... forecastTypes)
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.
forecastTypes - 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.CreateAutoPredictorRequest.Builder forecastDimensions(Collection<String> forecastDimensions)
An array of dimension (field) names that specify how to group the generated forecast.
For example, if you are generating forecasts for item sales across all your stores, and your dataset contains
a store_id field, you would specify store_id as a dimension to group sales
forecasts for each store.
forecastDimensions - An array of dimension (field) names that specify how to group the generated forecast.
For example, if you are generating forecasts for item sales across all your stores, and your dataset
contains a store_id field, you would specify store_id as a dimension to
group sales forecasts for each store.
CreateAutoPredictorRequest.Builder forecastDimensions(String... forecastDimensions)
An array of dimension (field) names that specify how to group the generated forecast.
For example, if you are generating forecasts for item sales across all your stores, and your dataset contains
a store_id field, you would specify store_id as a dimension to group sales
forecasts for each store.
forecastDimensions - An array of dimension (field) names that specify how to group the generated forecast.
For example, if you are generating forecasts for item sales across all your stores, and your dataset
contains a store_id field, you would specify store_id as a dimension to
group sales forecasts for each store.
CreateAutoPredictorRequest.Builder forecastFrequency(String forecastFrequency)
The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".
The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.
When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the RELATED_TIME_SERIES dataset frequency.
forecastFrequency - The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".
The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.
When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the RELATED_TIME_SERIES dataset frequency.
CreateAutoPredictorRequest.Builder dataConfig(DataConfig dataConfig)
The data configuration for your dataset group and any additional datasets.
dataConfig - The data configuration for your dataset group and any additional datasets.default CreateAutoPredictorRequest.Builder dataConfig(Consumer<DataConfig.Builder> dataConfig)
The data configuration for your dataset group and any additional datasets.
This is a convenience method that creates an instance of theDataConfig.Builder avoiding the need to
create one manually via DataConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its result
is passed to dataConfig(DataConfig).
dataConfig - a consumer that will call methods on DataConfig.BuilderdataConfig(DataConfig)CreateAutoPredictorRequest.Builder encryptionConfig(EncryptionConfig encryptionConfig)
encryptionConfig - The new value for the EncryptionConfig property for this object.default CreateAutoPredictorRequest.Builder encryptionConfig(Consumer<EncryptionConfig.Builder> encryptionConfig)
EncryptionConfig.Builder avoiding the
need to create one manually via EncryptionConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to encryptionConfig(EncryptionConfig).
encryptionConfig - a consumer that will call methods on EncryptionConfig.BuilderencryptionConfig(EncryptionConfig)CreateAutoPredictorRequest.Builder referencePredictorArn(String referencePredictorArn)
The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading a predictor. When creating a new predictor, do not specify a value for this parameter.
When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and
PredictorName. The value for PredictorName must be a unique predictor name.
referencePredictorArn - The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or
upgrading a predictor. When creating a new predictor, do not specify a value for this parameter.
When upgrading or retraining a predictor, only specify values for the
ReferencePredictorArn and PredictorName. The value for
PredictorName must be a unique predictor name.
CreateAutoPredictorRequest.Builder optimizationMetric(String optimizationMetric)
The accuracy metric used to optimize the predictor.
optimizationMetric - The accuracy metric used to optimize the predictor.OptimizationMetric,
OptimizationMetricCreateAutoPredictorRequest.Builder optimizationMetric(OptimizationMetric optimizationMetric)
The accuracy metric used to optimize the predictor.
optimizationMetric - The accuracy metric used to optimize the predictor.OptimizationMetric,
OptimizationMetricCreateAutoPredictorRequest.Builder explainPredictor(Boolean explainPredictor)
Create an Explainability resource for the predictor.
explainPredictor - Create an Explainability resource for the predictor.CreateAutoPredictorRequest.Builder tags(Collection<Tag> tags)
Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.
The following restrictions apply to tags:
For each resource, each tag key must be unique and each tag key must have one value.
Maximum number of tags per resource: 50.
Maximum key length: 128 Unicode characters in UTF-8.
Maximum value length: 256 Unicode characters in UTF-8.
Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.
Key prefixes cannot include any upper or lowercase combination of aws: or AWS:.
Values can have this prefix. If a tag value has aws as its prefix but the key does not, 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. You cannot edit or delete tag keys with
this prefix.
tags - Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and
an optional value, both of which you define. Tag keys and values are case sensitive.
The following restrictions apply to tags:
For each resource, each tag key must be unique and each tag key must have one value.
Maximum number of tags per resource: 50.
Maximum key length: 128 Unicode characters in UTF-8.
Maximum value length: 256 Unicode characters in UTF-8.
Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.
Key prefixes cannot include any upper or lowercase combination of aws: or
AWS:. Values can have this prefix. If a tag value has aws as its prefix but
the key does not, 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.
You cannot edit or delete tag keys with this prefix.
CreateAutoPredictorRequest.Builder tags(Tag... tags)
Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.
The following restrictions apply to tags:
For each resource, each tag key must be unique and each tag key must have one value.
Maximum number of tags per resource: 50.
Maximum key length: 128 Unicode characters in UTF-8.
Maximum value length: 256 Unicode characters in UTF-8.
Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.
Key prefixes cannot include any upper or lowercase combination of aws: or AWS:.
Values can have this prefix. If a tag value has aws as its prefix but the key does not, 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. You cannot edit or delete tag keys with
this prefix.
tags - Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and
an optional value, both of which you define. Tag keys and values are case sensitive.
The following restrictions apply to tags:
For each resource, each tag key must be unique and each tag key must have one value.
Maximum number of tags per resource: 50.
Maximum key length: 128 Unicode characters in UTF-8.
Maximum value length: 256 Unicode characters in UTF-8.
Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.
Key prefixes cannot include any upper or lowercase combination of aws: or
AWS:. Values can have this prefix. If a tag value has aws as its prefix but
the key does not, 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.
You cannot edit or delete tag keys with this prefix.
CreateAutoPredictorRequest.Builder tags(Consumer<Tag.Builder>... tags)
Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.
The following restrictions apply to tags:
For each resource, each tag key must be unique and each tag key must have one value.
Maximum number of tags per resource: 50.
Maximum key length: 128 Unicode characters in UTF-8.
Maximum value length: 256 Unicode characters in UTF-8.
Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.
Key prefixes cannot include any upper or lowercase combination of aws: or AWS:.
Values can have this prefix. If a tag value has aws as its prefix but the key does not, 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. You cannot edit or delete tag keys with
this prefix.
Tag.Builder avoiding the need to create one manually
via Tag.builder().
When the Consumer completes,
SdkBuilder.build() is called immediately and its
result is passed to #tags(List.
tags - a consumer that will call methods on
Tag.Builder#tags(java.util.Collection) CreateAutoPredictorRequest.Builder monitorConfig(MonitorConfig monitorConfig)
The configuration details for predictor monitoring. Provide a name for the monitor resource to enable predictor monitoring.
Predictor monitoring allows you to see how your predictor's performance changes over time. For more information, see Predictor Monitoring.
monitorConfig - The configuration details for predictor monitoring. Provide a name for the monitor resource to enable
predictor monitoring.
Predictor monitoring allows you to see how your predictor's performance changes over time. For more information, see Predictor Monitoring.
default CreateAutoPredictorRequest.Builder monitorConfig(Consumer<MonitorConfig.Builder> monitorConfig)
The configuration details for predictor monitoring. Provide a name for the monitor resource to enable predictor monitoring.
Predictor monitoring allows you to see how your predictor's performance changes over time. For more information, see Predictor Monitoring.
This is a convenience method that creates an instance of theMonitorConfig.Builder avoiding the need
to create one manually via MonitorConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to monitorConfig(MonitorConfig).
monitorConfig - a consumer that will call methods on MonitorConfig.BuildermonitorConfig(MonitorConfig)CreateAutoPredictorRequest.Builder timeAlignmentBoundary(TimeAlignmentBoundary timeAlignmentBoundary)
The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast frequency. Provide the unit of time and the time boundary as a key value pair. For more information on specifying a time boundary, see Specifying a Time Boundary. If you don't provide a time boundary, Forecast uses a set of Default Time Boundaries.
timeAlignmentBoundary - The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast
frequency. Provide the unit of time and the time boundary as a key value pair. For more information on
specifying a time boundary, see Specifying a Time Boundary. If you don't provide a time boundary, Forecast uses a set of Default Time Boundaries.default CreateAutoPredictorRequest.Builder timeAlignmentBoundary(Consumer<TimeAlignmentBoundary.Builder> timeAlignmentBoundary)
The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast frequency. Provide the unit of time and the time boundary as a key value pair. For more information on specifying a time boundary, see Specifying a Time Boundary. If you don't provide a time boundary, Forecast uses a set of Default Time Boundaries.
This is a convenience method that creates an instance of theTimeAlignmentBoundary.Builder avoiding
the need to create one manually via TimeAlignmentBoundary.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and
its result is passed to timeAlignmentBoundary(TimeAlignmentBoundary).
timeAlignmentBoundary - a consumer that will call methods on TimeAlignmentBoundary.BuildertimeAlignmentBoundary(TimeAlignmentBoundary)CreateAutoPredictorRequest.Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration)
overrideConfiguration in interface AwsRequest.BuilderCreateAutoPredictorRequest.Builder overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer)
overrideConfiguration in interface AwsRequest.BuilderCopyright © 2023. All rights reserved.