/* * Copyright 2018-2023 Amazon.com, Inc. or its affiliates. All Rights Reserved. * * Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with * the License. A copy of the License is located at * * http://aws.amazon.com/apache2.0 * * or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR * CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions * and limitations under the License. */ package com.amazonaws.services.sagemaker.model; import java.io.Serializable; import javax.annotation.Generated; import com.amazonaws.protocol.StructuredPojo; import com.amazonaws.protocol.ProtocolMarshaller; /** *

* 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 AWS API Documentation */ @Generated("com.amazonaws:aws-java-sdk-code-generator") public class TimeSeriesForecastingJobConfig implements Serializable, Cloneable, StructuredPojo { /** *

* 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. *

* */ private String featureSpecificationS3Uri; private AutoMLJobCompletionCriteria completionCriteria; /** *

* 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: *

* */ private String forecastFrequency; /** *

* 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. *

*/ private Integer forecastHorizon; /** *

* 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. *

*/ private java.util.List forecastQuantiles; /** *

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

*/ private TimeSeriesTransformations transformations; /** *

* The collection of components that defines the time-series. *

*/ private TimeSeriesConfig timeSeriesConfig; /** *

* 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. *

* * * @param 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. *

*/ public void setFeatureSpecificationS3Uri(String featureSpecificationS3Uri) { this.featureSpecificationS3Uri = 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. *

* * * @return 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. *

*/ public String getFeatureSpecificationS3Uri() { return this.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. *

* * * @param 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. *

* @return Returns a reference to this object so that method calls can be chained together. */ public TimeSeriesForecastingJobConfig withFeatureSpecificationS3Uri(String featureSpecificationS3Uri) { setFeatureSpecificationS3Uri(featureSpecificationS3Uri); return this; } /** * @param completionCriteria */ public void setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) { this.completionCriteria = completionCriteria; } /** * @return */ public AutoMLJobCompletionCriteria getCompletionCriteria() { return this.completionCriteria; } /** * @param completionCriteria * @return Returns a reference to this object so that method calls can be chained together. */ public TimeSeriesForecastingJobConfig withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) { setCompletionCriteria(completionCriteria); return this; } /** *

* 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 *

    *
    • *
* * @param 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 *

    *
    • */ public void setForecastFrequency(String forecastFrequency) { this.forecastFrequency = 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 *

      *
      • *
    * * @return 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 *

      *
      • */ public String getForecastFrequency() { return this.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 *

        *
        • *
      * * @param 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 *

        *
        • * @return Returns a reference to this object so that method calls can be chained together. */ public TimeSeriesForecastingJobConfig withForecastFrequency(String forecastFrequency) { setForecastFrequency(forecastFrequency); return this; } /** *

        * 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. *

        * * @param 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. */ public void setForecastHorizon(Integer forecastHorizon) { this.forecastHorizon = 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. *

        * * @return 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. */ public Integer getForecastHorizon() { return this.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. *

        * * @param 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. * @return Returns a reference to this object so that method calls can be chained together. */ public TimeSeriesForecastingJobConfig withForecastHorizon(Integer forecastHorizon) { setForecastHorizon(forecastHorizon); return this; } /** *

        * 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. *

        * * @return 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. */ public java.util.List getForecastQuantiles() { return 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. *

        * * @param 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. */ public void setForecastQuantiles(java.util.Collection forecastQuantiles) { if (forecastQuantiles == null) { this.forecastQuantiles = null; return; } this.forecastQuantiles = new java.util.ArrayList(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. *

        *

        * NOTE: This method appends the values to the existing list (if any). Use * {@link #setForecastQuantiles(java.util.Collection)} or {@link #withForecastQuantiles(java.util.Collection)} if * you want to override the existing values. *

        * * @param 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. * @return Returns a reference to this object so that method calls can be chained together. */ public TimeSeriesForecastingJobConfig withForecastQuantiles(String... forecastQuantiles) { if (this.forecastQuantiles == null) { setForecastQuantiles(new java.util.ArrayList(forecastQuantiles.length)); } for (String ele : forecastQuantiles) { this.forecastQuantiles.add(ele); } return this; } /** *

        * 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. *

        * * @param 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. * @return Returns a reference to this object so that method calls can be chained together. */ public TimeSeriesForecastingJobConfig withForecastQuantiles(java.util.Collection forecastQuantiles) { setForecastQuantiles(forecastQuantiles); return this; } /** *

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

        * * @param transformations * The transformations modifying specific attributes of the time-series, such as filling strategies for * missing values. */ public void setTransformations(TimeSeriesTransformations transformations) { this.transformations = transformations; } /** *

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

        * * @return The transformations modifying specific attributes of the time-series, such as filling strategies for * missing values. */ public TimeSeriesTransformations getTransformations() { return this.transformations; } /** *

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

        * * @param transformations * The transformations modifying specific attributes of the time-series, such as filling strategies for * missing values. * @return Returns a reference to this object so that method calls can be chained together. */ public TimeSeriesForecastingJobConfig withTransformations(TimeSeriesTransformations transformations) { setTransformations(transformations); return this; } /** *

        * The collection of components that defines the time-series. *

        * * @param timeSeriesConfig * The collection of components that defines the time-series. */ public void setTimeSeriesConfig(TimeSeriesConfig timeSeriesConfig) { this.timeSeriesConfig = timeSeriesConfig; } /** *

        * The collection of components that defines the time-series. *

        * * @return The collection of components that defines the time-series. */ public TimeSeriesConfig getTimeSeriesConfig() { return this.timeSeriesConfig; } /** *

        * The collection of components that defines the time-series. *

        * * @param timeSeriesConfig * The collection of components that defines the time-series. * @return Returns a reference to this object so that method calls can be chained together. */ public TimeSeriesForecastingJobConfig withTimeSeriesConfig(TimeSeriesConfig timeSeriesConfig) { setTimeSeriesConfig(timeSeriesConfig); return this; } /** * 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. * * @return A string representation of this object. * * @see java.lang.Object#toString() */ @Override public String toString() { StringBuilder sb = new StringBuilder(); sb.append("{"); if (getFeatureSpecificationS3Uri() != null) sb.append("FeatureSpecificationS3Uri: ").append(getFeatureSpecificationS3Uri()).append(","); if (getCompletionCriteria() != null) sb.append("CompletionCriteria: ").append(getCompletionCriteria()).append(","); if (getForecastFrequency() != null) sb.append("ForecastFrequency: ").append(getForecastFrequency()).append(","); if (getForecastHorizon() != null) sb.append("ForecastHorizon: ").append(getForecastHorizon()).append(","); if (getForecastQuantiles() != null) sb.append("ForecastQuantiles: ").append(getForecastQuantiles()).append(","); if (getTransformations() != null) sb.append("Transformations: ").append(getTransformations()).append(","); if (getTimeSeriesConfig() != null) sb.append("TimeSeriesConfig: ").append(getTimeSeriesConfig()); sb.append("}"); return sb.toString(); } @Override public boolean equals(Object obj) { if (this == obj) return true; if (obj == null) return false; if (obj instanceof TimeSeriesForecastingJobConfig == false) return false; TimeSeriesForecastingJobConfig other = (TimeSeriesForecastingJobConfig) obj; if (other.getFeatureSpecificationS3Uri() == null ^ this.getFeatureSpecificationS3Uri() == null) return false; if (other.getFeatureSpecificationS3Uri() != null && other.getFeatureSpecificationS3Uri().equals(this.getFeatureSpecificationS3Uri()) == false) return false; if (other.getCompletionCriteria() == null ^ this.getCompletionCriteria() == null) return false; if (other.getCompletionCriteria() != null && other.getCompletionCriteria().equals(this.getCompletionCriteria()) == false) return false; if (other.getForecastFrequency() == null ^ this.getForecastFrequency() == null) return false; if (other.getForecastFrequency() != null && other.getForecastFrequency().equals(this.getForecastFrequency()) == false) return false; if (other.getForecastHorizon() == null ^ this.getForecastHorizon() == null) return false; if (other.getForecastHorizon() != null && other.getForecastHorizon().equals(this.getForecastHorizon()) == false) return false; if (other.getForecastQuantiles() == null ^ this.getForecastQuantiles() == null) return false; if (other.getForecastQuantiles() != null && other.getForecastQuantiles().equals(this.getForecastQuantiles()) == false) return false; if (other.getTransformations() == null ^ this.getTransformations() == null) return false; if (other.getTransformations() != null && other.getTransformations().equals(this.getTransformations()) == false) return false; if (other.getTimeSeriesConfig() == null ^ this.getTimeSeriesConfig() == null) return false; if (other.getTimeSeriesConfig() != null && other.getTimeSeriesConfig().equals(this.getTimeSeriesConfig()) == false) return false; return true; } @Override public int hashCode() { final int prime = 31; int hashCode = 1; hashCode = prime * hashCode + ((getFeatureSpecificationS3Uri() == null) ? 0 : getFeatureSpecificationS3Uri().hashCode()); hashCode = prime * hashCode + ((getCompletionCriteria() == null) ? 0 : getCompletionCriteria().hashCode()); hashCode = prime * hashCode + ((getForecastFrequency() == null) ? 0 : getForecastFrequency().hashCode()); hashCode = prime * hashCode + ((getForecastHorizon() == null) ? 0 : getForecastHorizon().hashCode()); hashCode = prime * hashCode + ((getForecastQuantiles() == null) ? 0 : getForecastQuantiles().hashCode()); hashCode = prime * hashCode + ((getTransformations() == null) ? 0 : getTransformations().hashCode()); hashCode = prime * hashCode + ((getTimeSeriesConfig() == null) ? 0 : getTimeSeriesConfig().hashCode()); return hashCode; } @Override public TimeSeriesForecastingJobConfig clone() { try { return (TimeSeriesForecastingJobConfig) super.clone(); } catch (CloneNotSupportedException e) { throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e); } } @com.amazonaws.annotation.SdkInternalApi @Override public void marshall(ProtocolMarshaller protocolMarshaller) { com.amazonaws.services.sagemaker.model.transform.TimeSeriesForecastingJobConfigMarshaller.getInstance().marshall(this, protocolMarshaller); } }