com.amazonaws.services.forecast.model

Class AttributeConfig

java.lang.Object

com.amazonaws.services.forecast.model.AttributeConfig

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AttributeConfig
extends Object
implements Serializable, Cloneable, StructuredPojo

Provides information about the method used to transform attributes.

The following is an example using the RETAIL domain:

{

"AttributeName": "demand",

"Transformations": {"aggregation": "sum", "middlefill": "zero", "backfill": "zero"}

}

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors

Constructor and Description
AttributeConfig()

Method Summary

Modifier and Type	Method and Description
AttributeConfig	addTransformationsEntry(String key, String value) Add a single Transformations entry
AttributeConfig	clearTransformationsEntries() Removes all the entries added into Transformations.
AttributeConfig	clone()
boolean	equals(Object obj)
String	getAttributeName() The name of the attribute as specified in the schema.
Map<String,String>	getTransformations() The method parameters (key-value pairs), which are a map of override parameters.
int	hashCode()
void	marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller.
void	setAttributeName(String attributeName) The name of the attribute as specified in the schema.
void	setTransformations(Map<String,String> transformations) The method parameters (key-value pairs), which are a map of override parameters.
String	toString() Returns a string representation of this object.
AttributeConfig	withAttributeName(String attributeName) The name of the attribute as specified in the schema.
AttributeConfig	withTransformations(Map<String,String> transformations) The method parameters (key-value pairs), which are a map of override parameters.

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

AttributeConfig

public AttributeConfig()

Method Detail

setAttributeName

public void setAttributeName(String attributeName)

The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target time series and the related time series datasets. For example, for the RETAIL domain, the target is demand.

Parameters:

attributeName - The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target time series and the related time series datasets. For example, for the RETAIL domain, the target is demand.

getAttributeName

public String getAttributeName()

The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target time series and the related time series datasets. For example, for the RETAIL domain, the target is demand.

Returns:

The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target time series and the related time series datasets. For example, for the RETAIL domain, the target is demand.

withAttributeName

public AttributeConfig withAttributeName(String attributeName)

The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target time series and the related time series datasets. For example, for the RETAIL domain, the target is demand.

Parameters:

attributeName - The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target time series and the related time series datasets. For example, for the RETAIL domain, the target is demand.

Returns:

Returns a reference to this object so that method calls can be chained together.

getTransformations

public Map<String,String> getTransformations()

The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to override the default values. Related Time Series attributes do not accept aggregation parameters.

The following list shows the parameters and their valid values for the "filling" featurization method for a Target Time Series dataset. Default values are bolded.

• aggregation: sum, avg, first, min, max

• frontfill: none

• middlefill: zero, nan (not a number), value, median, mean, min, max

• backfill: zero, nan, value, median, mean, min, max

The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):

• middlefill: zero, value, median, mean, min, max

• backfill: zero, value, median, mean, min, max

• futurefill: zero, value, median, mean, min, max

To set a filling method to a specific value, set the fill parameter to value and define the value in a corresponding _value parameter. For example, to set backfilling to a value of 2, include the following: "backfill": "value" and "backfill_value":"2".

Returns:

The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to override the default values. Related Time Series attributes do not accept aggregation parameters.

The following list shows the parameters and their valid values for the "filling" featurization method for a Target Time Series dataset. Default values are bolded.

• aggregation: sum, avg, first, min, max

• frontfill: none

• middlefill: zero, nan (not a number), value, median, mean, min, max

• backfill: zero, nan, value, median, mean, min, max

The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):

• middlefill: zero, value, median, mean, min, max

• backfill: zero, value, median, mean, min, max

• futurefill: zero, value, median, mean, min, max

To set a filling method to a specific value, set the fill parameter to value and define the value in a corresponding _value parameter. For example, to set backfilling to a value of 2, include the following: "backfill": "value" and "backfill_value":"2".

setTransformations

public void setTransformations(Map<String,String> transformations)

The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to override the default values. Related Time Series attributes do not accept aggregation parameters.

The following list shows the parameters and their valid values for the "filling" featurization method for a Target Time Series dataset. Default values are bolded.

• aggregation: sum, avg, first, min, max

• frontfill: none

• middlefill: zero, nan (not a number), value, median, mean, min, max

• backfill: zero, nan, value, median, mean, min, max

The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):

• middlefill: zero, value, median, mean, min, max

• backfill: zero, value, median, mean, min, max

• futurefill: zero, value, median, mean, min, max

To set a filling method to a specific value, set the fill parameter to value and define the value in a corresponding _value parameter. For example, to set backfilling to a value of 2, include the following: "backfill": "value" and "backfill_value":"2".

Parameters:

transformations - The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to override the default values. Related Time Series attributes do not accept aggregation parameters.

The following list shows the parameters and their valid values for the "filling" featurization method for a Target Time Series dataset. Default values are bolded.

• aggregation: sum, avg, first, min, max

• frontfill: none

• middlefill: zero, nan (not a number), value, median, mean, min, max

• backfill: zero, nan, value, median, mean, min, max

The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):

• middlefill: zero, value, median, mean, min, max

• backfill: zero, value, median, mean, min, max

• futurefill: zero, value, median, mean, min, max

To set a filling method to a specific value, set the fill parameter to value and define the value in a corresponding _value parameter. For example, to set backfilling to a value of 2, include the following: "backfill": "value" and "backfill_value":"2".

withTransformations

public AttributeConfig withTransformations(Map<String,String> transformations)

The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to override the default values. Related Time Series attributes do not accept aggregation parameters.

The following list shows the parameters and their valid values for the "filling" featurization method for a Target Time Series dataset. Default values are bolded.

• aggregation: sum, avg, first, min, max

• frontfill: none

• middlefill: zero, nan (not a number), value, median, mean, min, max

• backfill: zero, nan, value, median, mean, min, max

The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):

• middlefill: zero, value, median, mean, min, max

• backfill: zero, value, median, mean, min, max

• futurefill: zero, value, median, mean, min, max

To set a filling method to a specific value, set the fill parameter to value and define the value in a corresponding _value parameter. For example, to set backfilling to a value of 2, include the following: "backfill": "value" and "backfill_value":"2".

Parameters:

transformations - The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to override the default values. Related Time Series attributes do not accept aggregation parameters.

The following list shows the parameters and their valid values for the "filling" featurization method for a Target Time Series dataset. Default values are bolded.

• aggregation: sum, avg, first, min, max

• frontfill: none

• middlefill: zero, nan (not a number), value, median, mean, min, max

• backfill: zero, nan, value, median, mean, min, max

The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):

• middlefill: zero, value, median, mean, min, max

• backfill: zero, value, median, mean, min, max

• futurefill: zero, value, median, mean, min, max

To set a filling method to a specific value, set the fill parameter to value and define the value in a corresponding _value parameter. For example, to set backfilling to a value of 2, include the following: "backfill": "value" and "backfill_value":"2".

Returns:

Returns a reference to this object so that method calls can be chained together.

addTransformationsEntry

public AttributeConfig addTransformationsEntry(String key,
                                               String value)

Add a single Transformations entry

See Also:

withTransformations(java.util.Map<java.lang.String, java.lang.String>)

clearTransformationsEntries

public AttributeConfig clearTransformationsEntries()

Removes all the entries added into Transformations.

Returns:

Returns a reference to this object so that method calls can be chained together.

toString

public 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

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public AttributeConfig clone()

Overrides:

clone in class Object

marshall

public void marshall(ProtocolMarshaller protocolMarshaller)

Description copied from interface: StructuredPojo

Marshalls this structured data using the given ProtocolMarshaller.

Specified by:

marshall in interface StructuredPojo

Parameters:

protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.