software.amazon.awssdk.services.sagemaker.model

Class TimeSeriesForecastingJobConfig

java.lang.Object

software.amazon.awssdk.services.sagemaker.model.TimeSeriesForecastingJobConfig

All Implemented Interfaces:

Serializable, SdkPojo, ToCopyableBuilder<TimeSeriesForecastingJobConfig.Builder,TimeSeriesForecastingJobConfig>

@Generated(value="software.amazon.awssdk:codegen")
public final class TimeSeriesForecastingJobConfig
extends Object
implements SdkPojo, Serializable, ToCopyableBuilder<TimeSeriesForecastingJobConfig.Builder,TimeSeriesForecastingJobConfig>

The collection of settings used by an AutoML job V2 for the time-series forecasting problem type.

The TimeSeriesForecastingJobConfig problem type is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	TimeSeriesForecastingJobConfig.Builder

Method Summary

Modifier and Type	Method and Description
static TimeSeriesForecastingJobConfig.Builder	builder()
AutoMLJobCompletionCriteria	completionCriteria() Returns the value of the CompletionCriteria property for this object.
boolean	equals(Object obj)
boolean	equalsBySdkFields(Object obj)
String	featureSpecificationS3Uri() A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig.
String	forecastFrequency() The frequency of predictions in a forecast.
Integer	forecastHorizon() The number of time-steps that the model predicts.
List<String>	forecastQuantiles() The quantiles used to train the model for forecasts at a specified quantile.
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
boolean	hasForecastQuantiles() For responses, this returns true if the service returned a value for the ForecastQuantiles property.
int	hashCode()
List<SdkField<?>>	sdkFields()
static Class<? extends TimeSeriesForecastingJobConfig.Builder>	serializableBuilderClass()
TimeSeriesConfig	timeSeriesConfig() The collection of components that defines the time-series.
TimeSeriesForecastingJobConfig.Builder	toBuilder()
String	toString() Returns a string representation of this object.
TimeSeriesTransformations	transformations() The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

copy

Method Detail

featureSpecificationS3Uri

public final String featureSpecificationS3Uri()

A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig.

You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

Autopilot supports the following data types: numeric, categorical, text, and datetime.

These column keys must not include any column set in TimeSeriesConfig.

Returns:

A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig. If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig.

You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

Autopilot supports the following data types: numeric, categorical, text, and datetime.

These column keys must not include any column set in TimeSeriesConfig.

completionCriteria

public final AutoMLJobCompletionCriteria completionCriteria()

Returns the value of the CompletionCriteria property for this object.

Returns:

The value of the CompletionCriteria property for this object.

forecastFrequency

public final 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. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min.

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

Returns:

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. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min.

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

forecastHorizon

public final 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 time-steps in the dataset.

Returns:

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 time-steps in the dataset.

hasForecastQuantiles

public final boolean hasForecastQuantiles()

For responses, this returns true if the service returned a value for the ForecastQuantiles 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.

forecastQuantiles

public final List<String> forecastQuantiles()

The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.

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 hasForecastQuantiles() method.

Returns:

The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.

transformations

public final TimeSeriesTransformations transformations()

The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.

Returns:

The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.

timeSeriesConfig

public final TimeSeriesConfig timeSeriesConfig()

The collection of components that defines the time-series.

Returns:

The collection of components that defines the time-series.

toBuilder

public TimeSeriesForecastingJobConfig.Builder toBuilder()

Specified by:

toBuilder in interface ToCopyableBuilder<TimeSeriesForecastingJobConfig.Builder,TimeSeriesForecastingJobConfig>

builder

public static TimeSeriesForecastingJobConfig.Builder builder()

serializableBuilderClass

public static Class<? extends TimeSeriesForecastingJobConfig.Builder> serializableBuilderClass()

hashCode

public final int hashCode()

Overrides:

hashCode in class Object

equals

public final boolean equals(Object obj)

Overrides:

equals in class Object

equalsBySdkFields

public final boolean equalsBySdkFields(Object obj)

Specified by:

equalsBySdkFields in interface SdkPojo

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

getValueForField

public final <T> Optional<T> getValueForField(String fieldName,
                                              Class<T> clazz)

sdkFields

public final List<SdkField<?>> sdkFields()

Specified by:

sdkFields in interface SdkPojo