software.amazon.awssdk.services.forecast.model

Interface FeaturizationConfig.Builder

All Superinterfaces:

Buildable, CopyableBuilder<FeaturizationConfig.Builder,FeaturizationConfig>, SdkBuilder<FeaturizationConfig.Builder,FeaturizationConfig>, SdkPojo

Enclosing class:

FeaturizationConfig

public static interface FeaturizationConfig.Builder
extends SdkPojo, CopyableBuilder<FeaturizationConfig.Builder,FeaturizationConfig>

Method Summary

Modifier and Type	Method and Description
FeaturizationConfig.Builder	featurizations(Collection<Featurization> featurizations) An array of featurization (transformation) information for the fields of a dataset.
FeaturizationConfig.Builder	featurizations(Consumer<Featurization.Builder>... featurizations) An array of featurization (transformation) information for the fields of a dataset.
FeaturizationConfig.Builder	featurizations(Featurization... featurizations) An array of featurization (transformation) information for the fields of a dataset.
FeaturizationConfig.Builder	forecastDimensions(Collection<String> forecastDimensions) An array of dimension (field) names that specify how to group the generated forecast.
FeaturizationConfig.Builder	forecastDimensions(String... forecastDimensions) An array of dimension (field) names that specify how to group the generated forecast.
FeaturizationConfig.Builder	forecastFrequency(String forecastFrequency) The frequency of predictions in a forecast.

Methods inherited from interface software.amazon.awssdk.core.SdkPojo

equalsBySdkFields, sdkFields

Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder

copy

Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder

applyMutation, build

Method Detail

forecastFrequency

FeaturizationConfig.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. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. 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

Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".

The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.

When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES dataset frequency.

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. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. 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

Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".

The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.

When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES dataset frequency.

Returns:

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

forecastDimensions

FeaturizationConfig.Builder forecastDimensions(Collection<String> forecastDimensions)

An array of dimension (field) names that specify how to group the generated forecast.

For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.

All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.

Parameters:

forecastDimensions - An array of dimension (field) names that specify how to group the generated forecast.

For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.

All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.

Returns:

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

forecastDimensions

FeaturizationConfig.Builder forecastDimensions(String... forecastDimensions)

An array of dimension (field) names that specify how to group the generated forecast.

For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.

All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.

Parameters:

forecastDimensions - An array of dimension (field) names that specify how to group the generated forecast.

For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.

All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.

Returns:

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

featurizations

FeaturizationConfig.Builder featurizations(Collection<Featurization> featurizations)

An array of featurization (transformation) information for the fields of a dataset.

Parameters:

featurizations - An array of featurization (transformation) information for the fields of a dataset.

Returns:

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

featurizations

FeaturizationConfig.Builder featurizations(Featurization... featurizations)

An array of featurization (transformation) information for the fields of a dataset.

Parameters:

featurizations - An array of featurization (transformation) information for the fields of a dataset.

Returns:

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

featurizations

FeaturizationConfig.Builder featurizations(Consumer<Featurization.Builder>... featurizations)

An array of featurization (transformation) information for the fields of a dataset.

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

When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to #featurizations(List).

Parameters:

featurizations - a consumer that will call methods on Featurization.Builder

Returns:

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

See Also:

#featurizations(java.util.Collection)