@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class CreateAutoPredictorRequest extends AmazonWebServiceRequest implements Serializable, Cloneable
NOOP| Constructor and Description |
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CreateAutoPredictorRequest() |
| Modifier and Type | Method and Description |
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CreateAutoPredictorRequest |
clone()
Creates a shallow clone of this object for all fields except the handler context.
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boolean |
equals(Object obj) |
DataConfig |
getDataConfig()
The data configuration for your dataset group and any additional datasets.
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EncryptionConfig |
getEncryptionConfig() |
Boolean |
getExplainPredictor()
Create an Explainability resource for the predictor.
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List<String> |
getForecastDimensions()
An array of dimension (field) names that specify how to group the generated forecast.
|
String |
getForecastFrequency()
The frequency of predictions in a forecast.
|
Integer |
getForecastHorizon()
The number of time-steps that the model predicts.
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List<String> |
getForecastTypes()
The forecast types used to train a predictor.
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MonitorConfig |
getMonitorConfig()
The configuration details for predictor monitoring.
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String |
getOptimizationMetric()
The accuracy metric used to optimize the predictor.
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String |
getPredictorName()
A unique name for the predictor
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String |
getReferencePredictorArn()
The ARN of the predictor to retrain or upgrade.
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List<Tag> |
getTags()
Optional metadata to help you categorize and organize your predictors.
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TimeAlignmentBoundary |
getTimeAlignmentBoundary()
The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast frequency.
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int |
hashCode() |
Boolean |
isExplainPredictor()
Create an Explainability resource for the predictor.
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void |
setDataConfig(DataConfig dataConfig)
The data configuration for your dataset group and any additional datasets.
|
void |
setEncryptionConfig(EncryptionConfig encryptionConfig) |
void |
setExplainPredictor(Boolean explainPredictor)
Create an Explainability resource for the predictor.
|
void |
setForecastDimensions(Collection<String> forecastDimensions)
An array of dimension (field) names that specify how to group the generated forecast.
|
void |
setForecastFrequency(String forecastFrequency)
The frequency of predictions in a forecast.
|
void |
setForecastHorizon(Integer forecastHorizon)
The number of time-steps that the model predicts.
|
void |
setForecastTypes(Collection<String> forecastTypes)
The forecast types used to train a predictor.
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void |
setMonitorConfig(MonitorConfig monitorConfig)
The configuration details for predictor monitoring.
|
void |
setOptimizationMetric(String optimizationMetric)
The accuracy metric used to optimize the predictor.
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void |
setPredictorName(String predictorName)
A unique name for the predictor
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void |
setReferencePredictorArn(String referencePredictorArn)
The ARN of the predictor to retrain or upgrade.
|
void |
setTags(Collection<Tag> tags)
Optional metadata to help you categorize and organize your predictors.
|
void |
setTimeAlignmentBoundary(TimeAlignmentBoundary timeAlignmentBoundary)
The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast frequency.
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String |
toString()
Returns a string representation of this object.
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CreateAutoPredictorRequest |
withDataConfig(DataConfig dataConfig)
The data configuration for your dataset group and any additional datasets.
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CreateAutoPredictorRequest |
withEncryptionConfig(EncryptionConfig encryptionConfig) |
CreateAutoPredictorRequest |
withExplainPredictor(Boolean explainPredictor)
Create an Explainability resource for the predictor.
|
CreateAutoPredictorRequest |
withForecastDimensions(Collection<String> forecastDimensions)
An array of dimension (field) names that specify how to group the generated forecast.
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CreateAutoPredictorRequest |
withForecastDimensions(String... forecastDimensions)
An array of dimension (field) names that specify how to group the generated forecast.
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CreateAutoPredictorRequest |
withForecastFrequency(String forecastFrequency)
The frequency of predictions in a forecast.
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CreateAutoPredictorRequest |
withForecastHorizon(Integer forecastHorizon)
The number of time-steps that the model predicts.
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CreateAutoPredictorRequest |
withForecastTypes(Collection<String> forecastTypes)
The forecast types used to train a predictor.
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CreateAutoPredictorRequest |
withForecastTypes(String... forecastTypes)
The forecast types used to train a predictor.
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CreateAutoPredictorRequest |
withMonitorConfig(MonitorConfig monitorConfig)
The configuration details for predictor monitoring.
|
CreateAutoPredictorRequest |
withOptimizationMetric(OptimizationMetric optimizationMetric)
The accuracy metric used to optimize the predictor.
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CreateAutoPredictorRequest |
withOptimizationMetric(String optimizationMetric)
The accuracy metric used to optimize the predictor.
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CreateAutoPredictorRequest |
withPredictorName(String predictorName)
A unique name for the predictor
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CreateAutoPredictorRequest |
withReferencePredictorArn(String referencePredictorArn)
The ARN of the predictor to retrain or upgrade.
|
CreateAutoPredictorRequest |
withTags(Collection<Tag> tags)
Optional metadata to help you categorize and organize your predictors.
|
CreateAutoPredictorRequest |
withTags(Tag... tags)
Optional metadata to help you categorize and organize your predictors.
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CreateAutoPredictorRequest |
withTimeAlignmentBoundary(TimeAlignmentBoundary timeAlignmentBoundary)
The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast frequency.
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addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeoutpublic void setPredictorName(String predictorName)
A unique name for the predictor
predictorName - A unique name for the predictorpublic String getPredictorName()
A unique name for the predictor
public CreateAutoPredictorRequest withPredictorName(String predictorName)
A unique name for the predictor
predictorName - A unique name for the predictorpublic void setForecastHorizon(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.
public Integer getForecastHorizon()
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.
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.
public CreateAutoPredictorRequest withForecastHorizon(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.
public List<String> getForecastTypes()
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.
mean.public void setForecastTypes(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.public CreateAutoPredictorRequest withForecastTypes(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.
NOTE: This method appends the values to the existing list (if any). Use
setForecastTypes(java.util.Collection) or withForecastTypes(java.util.Collection) if you want
to override the existing values.
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.public CreateAutoPredictorRequest withForecastTypes(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.public List<String> getForecastDimensions()
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.
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.
public void setForecastDimensions(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.
public CreateAutoPredictorRequest withForecastDimensions(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.
NOTE: This method appends the values to the existing list (if any). Use
setForecastDimensions(java.util.Collection) or withForecastDimensions(java.util.Collection) if
you want to override the existing values.
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.
public CreateAutoPredictorRequest withForecastDimensions(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.
public void setForecastFrequency(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.
public String getForecastFrequency()
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.
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.
public CreateAutoPredictorRequest withForecastFrequency(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.
public void setDataConfig(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.public DataConfig getDataConfig()
The data configuration for your dataset group and any additional datasets.
public CreateAutoPredictorRequest withDataConfig(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.public void setEncryptionConfig(EncryptionConfig encryptionConfig)
encryptionConfig - public EncryptionConfig getEncryptionConfig()
public CreateAutoPredictorRequest withEncryptionConfig(EncryptionConfig encryptionConfig)
encryptionConfig - public void setReferencePredictorArn(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.
public String getReferencePredictorArn()
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.
When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn
and PredictorName. The value for PredictorName must be a unique predictor name.
public CreateAutoPredictorRequest withReferencePredictorArn(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.
public void setOptimizationMetric(String optimizationMetric)
The accuracy metric used to optimize the predictor.
optimizationMetric - The accuracy metric used to optimize the predictor.OptimizationMetricpublic String getOptimizationMetric()
The accuracy metric used to optimize the predictor.
OptimizationMetricpublic CreateAutoPredictorRequest withOptimizationMetric(String optimizationMetric)
The accuracy metric used to optimize the predictor.
optimizationMetric - The accuracy metric used to optimize the predictor.OptimizationMetricpublic CreateAutoPredictorRequest withOptimizationMetric(OptimizationMetric optimizationMetric)
The accuracy metric used to optimize the predictor.
optimizationMetric - The accuracy metric used to optimize the predictor.OptimizationMetricpublic void setExplainPredictor(Boolean explainPredictor)
Create an Explainability resource for the predictor.
explainPredictor - Create an Explainability resource for the predictor.public Boolean getExplainPredictor()
Create an Explainability resource for the predictor.
public CreateAutoPredictorRequest withExplainPredictor(Boolean explainPredictor)
Create an Explainability resource for the predictor.
explainPredictor - Create an Explainability resource for the predictor.public Boolean isExplainPredictor()
Create an Explainability resource for the predictor.
public List<Tag> getTags()
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.
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.
public void setTags(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.
public CreateAutoPredictorRequest withTags(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.
NOTE: This method appends the values to the existing list (if any). Use
setTags(java.util.Collection) or withTags(java.util.Collection) if you want to override the
existing values.
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.
public CreateAutoPredictorRequest withTags(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.
public void setMonitorConfig(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.
public MonitorConfig getMonitorConfig()
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.
Predictor monitoring allows you to see how your predictor's performance changes over time. For more information, see Predictor Monitoring.
public CreateAutoPredictorRequest withMonitorConfig(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.
public void setTimeAlignmentBoundary(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.public TimeAlignmentBoundary getTimeAlignmentBoundary()
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.
public CreateAutoPredictorRequest withTimeAlignmentBoundary(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.public String toString()
toString in class ObjectObject.toString()public CreateAutoPredictorRequest clone()
AmazonWebServiceRequestclone in class AmazonWebServiceRequestObject.clone()