Uses of Interface
software.amazon.awssdk.services.sagemaker.model.TimeSeriesTransformations.Builder

Packages that use TimeSeriesTransformations.Builder

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

Uses of TimeSeriesTransformations.Builder in software.amazon.awssdk.services.sagemaker.model

Methods in software.amazon.awssdk.services.sagemaker.model that return TimeSeriesTransformations.Builder

Modifier and Type	Method and Description
TimeSeriesTransformations.Builder	TimeSeriesTransformations.Builder.aggregation(Map<String,AggregationTransformationValue> aggregation) A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.
TimeSeriesTransformations.Builder	TimeSeriesTransformations.Builder.aggregationWithStrings(Map<String,String> aggregation) A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.
static TimeSeriesTransformations.Builder	TimeSeriesTransformations.builder()
TimeSeriesTransformations.Builder	TimeSeriesTransformations.Builder.fillingWithStrings(Map<String,? extends Map<String,String>> filling) A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic.
TimeSeriesTransformations.Builder	TimeSeriesTransformations.toBuilder()

Methods in software.amazon.awssdk.services.sagemaker.model that return types with arguments of type TimeSeriesTransformations.Builder

Modifier and Type	Method and Description
static Class<? extends TimeSeriesTransformations.Builder>	TimeSeriesTransformations.serializableBuilderClass()

Method parameters in software.amazon.awssdk.services.sagemaker.model with type arguments of type TimeSeriesTransformations.Builder

Modifier and Type	Method and Description
default TimeSeriesForecastingJobConfig.Builder	TimeSeriesForecastingJobConfig.Builder.transformations(Consumer<TimeSeriesTransformations.Builder> transformations) The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.