public static interface FeaturizationConfig.Builder extends SdkPojo, CopyableBuilder<FeaturizationConfig.Builder,FeaturizationConfig>
| 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.
|
equalsBySdkFields, sdkFieldscopyapplyMutation, buildFeaturizationConfig.Builder forecastFrequency(String forecastFrequency)
The frequency of predictions in a forecast.
Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "Y" indicates every year and "5min" indicates every five minutes.
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 RELATED_TIME_SERIES dataset frequency.
forecastFrequency - The frequency of predictions in a forecast.
Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "Y" indicates every year and "5min" indicates every five minutes.
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 RELATED_TIME_SERIES dataset frequency.
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.
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.
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.
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.
FeaturizationConfig.Builder featurizations(Collection<Featurization> featurizations)
An array of featurization (transformation) information for the fields of a dataset.
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.
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.
This is a convenience that creates an instance of theList.Builder avoiding the need
to create one manually via List#builder() .
When the Consumer completes, List.Builder#build() is called immediately and
its result is passed to #featurizations(List) .featurizations - a consumer that will call methods on List.Builder #featurizations(List) Copyright © 2020. All rights reserved.