com.ibm.watson.discovery.v1.model

Class Timeslice

java.lang.Object

com.ibm.cloud.sdk.core.service.model.GenericModel

com.ibm.watson.discovery.v1.model.QueryAggregation

com.ibm.watson.discovery.v1.model.Timeslice

All Implemented Interfaces:

com.ibm.cloud.sdk.core.service.model.ObjectModel

public class Timeslice
extends QueryAggregation

Timeslice.

Field Summary

Fields

Modifier and Type	Field and Description
protected java.lang.Boolean	anomaly
protected java.lang.String	field
protected java.lang.String	interval

Fields inherited from class com.ibm.watson.discovery.v1.model.QueryAggregation

aggregations, discriminatorMapping, discriminatorPropertyName, matchingResults, results, type

Constructor Summary

Constructors

Constructor and Description
Timeslice()

Method Summary

Methods

Modifier and Type	Method and Description
java.lang.String	getField() Gets the field.
java.lang.String	getInterval() Gets the interval.
java.lang.Boolean	isAnomaly() Gets the anomaly.

Methods inherited from class com.ibm.watson.discovery.v1.model.QueryAggregation

getAggregations, getMatchingResults, getResults, getType

Methods inherited from class com.ibm.cloud.sdk.core.service.model.GenericModel

equals, hashCode, toString

Methods inherited from class java.lang.Object

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

Field Detail

field

protected java.lang.String field

interval

protected java.lang.String interval

anomaly

protected java.lang.Boolean anomaly

Constructor Detail

Timeslice

public Timeslice()

Method Detail

getField

public java.lang.String getField()

Gets the field. The field where the aggregation is located in the document.

Returns:

the field

getInterval

public java.lang.String getInterval()

Gets the interval. Interval of the aggregation. Valid date interval values are second/seconds minute/minutes, hour/hours, day/days, week/weeks, month/months, and year/years.

Returns:

the interval

isAnomaly

public java.lang.Boolean isAnomaly()

Gets the anomaly. Used to indicate that anomaly detection should be performed. Anomaly detection is used to locate unusual datapoints within a time series.

Returns:

the anomaly