com.amazonaws.services.forecast.model

Class EvaluationParameters

java.lang.Object

com.amazonaws.services.forecast.model.EvaluationParameters

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable

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

Parameters that define how to split a dataset into training data and testing data, and the number of iterations to perform. These parameters are specified in the predefined algorithms but you can override them in the CreatePredictor request.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors

Constructor and Description
EvaluationParameters()

Method Summary

Modifier and Type	Method and Description
EvaluationParameters	clone()
boolean	equals(Object obj)
Integer	getBackTestWindowOffset() The point from the end of the dataset where you want to split the data for model training and testing (evaluation).
Integer	getNumberOfBacktestWindows() The number of times to split the input data.
int	hashCode()
void	marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller.
void	setBackTestWindowOffset(Integer backTestWindowOffset) The point from the end of the dataset where you want to split the data for model training and testing (evaluation).
void	setNumberOfBacktestWindows(Integer numberOfBacktestWindows) The number of times to split the input data.
String	toString() Returns a string representation of this object.
EvaluationParameters	withBackTestWindowOffset(Integer backTestWindowOffset) The point from the end of the dataset where you want to split the data for model training and testing (evaluation).
EvaluationParameters	withNumberOfBacktestWindows(Integer numberOfBacktestWindows) The number of times to split the input data.

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

EvaluationParameters

public EvaluationParameters()

Method Detail

setNumberOfBacktestWindows

public void setNumberOfBacktestWindows(Integer numberOfBacktestWindows)

The number of times to split the input data. The default is 1. Valid values are 1 through 5.

Parameters:

numberOfBacktestWindows - The number of times to split the input data. The default is 1. Valid values are 1 through 5.

getNumberOfBacktestWindows

public Integer getNumberOfBacktestWindows()

The number of times to split the input data. The default is 1. Valid values are 1 through 5.

Returns:

The number of times to split the input data. The default is 1. Valid values are 1 through 5.

withNumberOfBacktestWindows

public EvaluationParameters withNumberOfBacktestWindows(Integer numberOfBacktestWindows)

The number of times to split the input data. The default is 1. Valid values are 1 through 5.

Parameters:

numberOfBacktestWindows - The number of times to split the input data. The default is 1. Valid values are 1 through 5.

Returns:

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

setBackTestWindowOffset

public void setBackTestWindowOffset(Integer backTestWindowOffset)

The point from the end of the dataset where you want to split the data for model training and testing (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon. BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.

ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length

Parameters:

backTestWindowOffset - The point from the end of the dataset where you want to split the data for model training and testing (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon. BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.

ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length

getBackTestWindowOffset

public Integer getBackTestWindowOffset()

The point from the end of the dataset where you want to split the data for model training and testing (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon. BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.

ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length

Returns:

The point from the end of the dataset where you want to split the data for model training and testing (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon. BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.

ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length

withBackTestWindowOffset

public EvaluationParameters withBackTestWindowOffset(Integer backTestWindowOffset)

The point from the end of the dataset where you want to split the data for model training and testing (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon. BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.

ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length

Parameters:

backTestWindowOffset - The point from the end of the dataset where you want to split the data for model training and testing (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon. BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.

ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length

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 EvaluationParameters 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.