com.google.api.services.bigquery.model

Class ArimaSingleModelForecastingMetrics

java.lang.Object

java.util.AbstractMap<String,Object>

com.google.api.client.util.GenericData

com.google.api.client.json.GenericJson

com.google.api.services.bigquery.model.ArimaSingleModelForecastingMetrics

All Implemented Interfaces:

Cloneable, Map<String,Object>

public final class ArimaSingleModelForecastingMetrics
extends GenericJson

Model evaluation metrics for a single ARIMA forecasting model.

This is the Java data model class that specifies how to parse/serialize into the JSON that is transmitted over HTTP when working with the BigQuery API. For a detailed explanation see: https://developers.google.com/api-client-library/java/google-http-java-client/json

Author:

Google, Inc.

Nested Class Summary

Nested classes/interfaces inherited from class com.google.api.client.util.GenericData

GenericData.Flags

Nested classes/interfaces inherited from class java.util.AbstractMap

AbstractMap.SimpleEntry<K,V>, AbstractMap.SimpleImmutableEntry<K,V>

Nested classes/interfaces inherited from interface java.util.Map

Map.Entry<K,V>

Constructor Summary

Constructors

Constructor and Description
ArimaSingleModelForecastingMetrics()

Method Summary

Modifier and Type	Method and Description
ArimaSingleModelForecastingMetrics	clone()
ArimaFittingMetrics	getArimaFittingMetrics() Arima fitting metrics.
Boolean	getHasDrift() Is arima model fitted with drift or not.
Boolean	getHasHolidayEffect() If true, holiday_effect is a part of time series decomposition result.
Boolean	getHasSpikesAndDips() If true, spikes_and_dips is a part of time series decomposition result.
Boolean	getHasStepChanges() If true, step_changes is a part of time series decomposition result.
ArimaOrder	getNonSeasonalOrder() Non-seasonal order.
List<String>	getSeasonalPeriods() Seasonal periods.
String	getTimeSeriesId() The time_series_id value for this time series.
List<String>	getTimeSeriesIds() The tuple of time_series_ids identifying this time series.
ArimaSingleModelForecastingMetrics	set(String fieldName, Object value)
ArimaSingleModelForecastingMetrics	setArimaFittingMetrics(ArimaFittingMetrics arimaFittingMetrics) Arima fitting metrics.
ArimaSingleModelForecastingMetrics	setHasDrift(Boolean hasDrift) Is arima model fitted with drift or not.
ArimaSingleModelForecastingMetrics	setHasHolidayEffect(Boolean hasHolidayEffect) If true, holiday_effect is a part of time series decomposition result.
ArimaSingleModelForecastingMetrics	setHasSpikesAndDips(Boolean hasSpikesAndDips) If true, spikes_and_dips is a part of time series decomposition result.
ArimaSingleModelForecastingMetrics	setHasStepChanges(Boolean hasStepChanges) If true, step_changes is a part of time series decomposition result.
ArimaSingleModelForecastingMetrics	setNonSeasonalOrder(ArimaOrder nonSeasonalOrder) Non-seasonal order.
ArimaSingleModelForecastingMetrics	setSeasonalPeriods(List<String> seasonalPeriods) Seasonal periods.
ArimaSingleModelForecastingMetrics	setTimeSeriesId(String timeSeriesId) The time_series_id value for this time series.
ArimaSingleModelForecastingMetrics	setTimeSeriesIds(List<String> timeSeriesIds) The tuple of time_series_ids identifying this time series.

Methods inherited from class com.google.api.client.json.GenericJson

getFactory, setFactory, toPrettyString, toString

Methods inherited from class com.google.api.client.util.GenericData

entrySet, equals, get, getClassInfo, getUnknownKeys, hashCode, put, putAll, remove, setUnknownKeys

Methods inherited from class java.util.AbstractMap

clear, containsKey, containsValue, isEmpty, keySet, size, values

Methods inherited from class java.lang.Object

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

Methods inherited from interface java.util.Map

compute, computeIfAbsent, computeIfPresent, forEach, getOrDefault, merge, putIfAbsent, remove, replace, replace, replaceAll

Constructor Detail

ArimaSingleModelForecastingMetrics

public ArimaSingleModelForecastingMetrics()

Method Detail

getArimaFittingMetrics

public ArimaFittingMetrics getArimaFittingMetrics()

Arima fitting metrics.

Returns:

value or null for none

setArimaFittingMetrics

public ArimaSingleModelForecastingMetrics setArimaFittingMetrics(ArimaFittingMetrics arimaFittingMetrics)

Arima fitting metrics.

Parameters:

arimaFittingMetrics - arimaFittingMetrics or null for none

getHasDrift

public Boolean getHasDrift()

Is arima model fitted with drift or not. It is always false when d is not 1.

Returns:

value or null for none

setHasDrift

public ArimaSingleModelForecastingMetrics setHasDrift(Boolean hasDrift)

Is arima model fitted with drift or not. It is always false when d is not 1.

Parameters:

hasDrift - hasDrift or null for none

getHasHolidayEffect

public Boolean getHasHolidayEffect()

If true, holiday_effect is a part of time series decomposition result.

Returns:

value or null for none

setHasHolidayEffect

public ArimaSingleModelForecastingMetrics setHasHolidayEffect(Boolean hasHolidayEffect)

If true, holiday_effect is a part of time series decomposition result.

Parameters:

hasHolidayEffect - hasHolidayEffect or null for none

getHasSpikesAndDips

public Boolean getHasSpikesAndDips()

If true, spikes_and_dips is a part of time series decomposition result.

Returns:

value or null for none

setHasSpikesAndDips

public ArimaSingleModelForecastingMetrics setHasSpikesAndDips(Boolean hasSpikesAndDips)

If true, spikes_and_dips is a part of time series decomposition result.

Parameters:

hasSpikesAndDips - hasSpikesAndDips or null for none

getHasStepChanges

public Boolean getHasStepChanges()

If true, step_changes is a part of time series decomposition result.

Returns:

value or null for none

setHasStepChanges

public ArimaSingleModelForecastingMetrics setHasStepChanges(Boolean hasStepChanges)

If true, step_changes is a part of time series decomposition result.

Parameters:

hasStepChanges - hasStepChanges or null for none

getNonSeasonalOrder

public ArimaOrder getNonSeasonalOrder()

Non-seasonal order.

Returns:

value or null for none

setNonSeasonalOrder

public ArimaSingleModelForecastingMetrics setNonSeasonalOrder(ArimaOrder nonSeasonalOrder)

Non-seasonal order.

Parameters:

nonSeasonalOrder - nonSeasonalOrder or null for none

getSeasonalPeriods

public List<String> getSeasonalPeriods()

Seasonal periods. Repeated because multiple periods are supported for one time series.

Returns:

value or null for none

setSeasonalPeriods

public ArimaSingleModelForecastingMetrics setSeasonalPeriods(List<String> seasonalPeriods)

Seasonal periods. Repeated because multiple periods are supported for one time series.

Parameters:

seasonalPeriods - seasonalPeriods or null for none

getTimeSeriesId

public String getTimeSeriesId()

The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.

Returns:

value or null for none

setTimeSeriesId

public ArimaSingleModelForecastingMetrics setTimeSeriesId(String timeSeriesId)

The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.

Parameters:

timeSeriesId - timeSeriesId or null for none

getTimeSeriesIds

public List<String> getTimeSeriesIds()

The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.

Returns:

value or null for none

setTimeSeriesIds

public ArimaSingleModelForecastingMetrics setTimeSeriesIds(List<String> timeSeriesIds)

The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.

Parameters:

timeSeriesIds - timeSeriesIds or null for none

set

public ArimaSingleModelForecastingMetrics set(String fieldName,
                                              Object value)

Overrides:

set in class GenericJson

clone

public ArimaSingleModelForecastingMetrics clone()

Overrides:

clone in class GenericJson