Package software.amazon.awssdk.services.sagemaker.model

Interface TimeSeriesForecastingJobConfig.Builder

All Superinterfaces:
Buildable, CopyableBuilder<TimeSeriesForecastingJobConfig.Builder,TimeSeriesForecastingJobConfig>, SdkBuilder<TimeSeriesForecastingJobConfig.Builder,TimeSeriesForecastingJobConfig>, SdkPojo
Enclosing class:
TimeSeriesForecastingJobConfig
public static interface TimeSeriesForecastingJobConfig.Builder extends SdkPojo, CopyableBuilder<TimeSeriesForecastingJobConfig.Builder,TimeSeriesForecastingJobConfig>

  • Method Details

    • featureSpecificationS3Uri

      TimeSeriesForecastingJobConfig.Builder featureSpecificationS3Uri(String 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.

      Parameters:
      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.

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

    • completionCriteria

      TimeSeriesForecastingJobConfig.Builder completionCriteria(AutoMLJobCompletionCriteria completionCriteria)
      Sets the value of the CompletionCriteria property for this object.
      Parameters:
      completionCriteria - The new value for the CompletionCriteria property for this object.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • completionCriteria

      default TimeSeriesForecastingJobConfig.Builder completionCriteria(Consumer<AutoMLJobCompletionCriteria.Builder> completionCriteria)
      Sets the value of the CompletionCriteria property for this object. This is a convenience method that creates an instance of the AutoMLJobCompletionCriteria.Builder avoiding the need to create one manually via AutoMLJobCompletionCriteria.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to completionCriteria(AutoMLJobCompletionCriteria).

      Parameters:
      completionCriteria - a consumer that will call methods on AutoMLJobCompletionCriteria.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • forecastFrequency

      TimeSeriesForecastingJobConfig.Builder forecastFrequency(String 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

      Parameters:
      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

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

    • forecastHorizon

      TimeSeriesForecastingJobConfig.Builder forecastHorizon(Integer 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.

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

    • forecastQuantiles

      TimeSeriesForecastingJobConfig.Builder forecastQuantiles(Collection<String> 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.

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

    • forecastQuantiles

      TimeSeriesForecastingJobConfig.Builder forecastQuantiles(String... 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.

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

    • transformations

      TimeSeriesForecastingJobConfig.Builder transformations(TimeSeriesTransformations transformations)

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

      Parameters:
      transformations - The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • transformations

      default TimeSeriesForecastingJobConfig.Builder transformations(Consumer<TimeSeriesTransformations.Builder> transformations)

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

      This is a convenience method that creates an instance of the TimeSeriesTransformations.Builder avoiding the need to create one manually via TimeSeriesTransformations.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to transformations(TimeSeriesTransformations).

      Parameters:
      transformations - a consumer that will call methods on TimeSeriesTransformations.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • timeSeriesConfig

      TimeSeriesForecastingJobConfig.Builder timeSeriesConfig(TimeSeriesConfig timeSeriesConfig)

      The collection of components that defines the time-series.

      Parameters:
      timeSeriesConfig - The collection of components that defines the time-series.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • timeSeriesConfig

      default TimeSeriesForecastingJobConfig.Builder timeSeriesConfig(Consumer<TimeSeriesConfig.Builder> timeSeriesConfig)

      The collection of components that defines the time-series.

      This is a convenience method that creates an instance of the TimeSeriesConfig.Builder avoiding the need to create one manually via TimeSeriesConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to timeSeriesConfig(TimeSeriesConfig).

      Parameters:
      timeSeriesConfig - a consumer that will call methods on TimeSeriesConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • holidayConfig

      TimeSeriesForecastingJobConfig.Builder holidayConfig(Collection<HolidayConfigAttributes> holidayConfig)

      The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.

      Parameters:
      holidayConfig - The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • holidayConfig

      TimeSeriesForecastingJobConfig.Builder holidayConfig(HolidayConfigAttributes... holidayConfig)

      The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.

      Parameters:
      holidayConfig - The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • holidayConfig

      TimeSeriesForecastingJobConfig.Builder holidayConfig(Consumer<HolidayConfigAttributes.Builder>... holidayConfig)

      The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.

      This is a convenience method that creates an instance of the HolidayConfigAttributes.Builder avoiding the need to create one manually via HolidayConfigAttributes.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to holidayConfig(List<HolidayConfigAttributes>).

      Parameters:
      holidayConfig - a consumer that will call methods on HolidayConfigAttributes.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also: